Loading [MathJax]/jax/output/SVG/jax.js
Research article

DyCARS: A dynamic context-aware recommendation system


  • Dynamic recommendation systems aim to achieve real-time updates and dynamic migration of user interests, primarily utilizing user-item interaction sequences with timestamps to capture the dynamic changes in user interests and item attributes. Recent research has mainly centered on two aspects. First, it involves modeling the dynamic interaction relationships between users and items using dynamic graphs. Second, it focuses on mining their long-term and short-term interaction patterns. This is achieved through the joint learning of static and dynamic embeddings for both users and items. Although most existing methods have achieved some success in modeling the historical interaction sequences between users and items, there is still room for improvement, particularly in terms of modeling the long-term dependency structures of dynamic interaction histories and extracting the most relevant delayed interaction patterns. To address this issue, we proposed a Dynamic Context-Aware Recommendation System for dynamic recommendation. Specifically, our model is built on a dynamic graph and utilizes the static embeddings of recent user-item interactions as dynamic context. Additionally, we constructed a Gated Multi-Layer Perceptron encoder to capture the long-term dependency structure in the dynamic interaction history and extract high-level features. Then, we introduced an Attention Pooling network to learn similarity scores between high-level features in the user-item dynamic interaction history. By calculating bidirectional attention weights, we extracted the most relevant delayed interaction patterns from the historical sequence to predict the dynamic embeddings of users and items. Additionally, we proposed a loss function called the Pairwise Cosine Similarity loss for dynamic recommendation to jointly optimize the static and dynamic embeddings of two types of nodes. Finally, extensive experiments on two real-world datasets, LastFM, and the Global Terrorism Database showed that our model achieves consistent improvements over state-of-the-art baselines.

    Citation: Zhiwen Hou, Fanliang Bu, Yuchen Zhou, Lingbin Bu, Qiming Ma, Yifan Wang, Hanming Zhai, Zhuxuan Han. DyCARS: A dynamic context-aware recommendation system[J]. Mathematical Biosciences and Engineering, 2024, 21(3): 3563-3593. doi: 10.3934/mbe.2024157

    Related Papers:

    [1] Toyohiko Aiki, Kota Kumazaki . Uniqueness of solutions to a mathematical model describing moisture transport in concrete materials. Networks and Heterogeneous Media, 2014, 9(4): 683-707. doi: 10.3934/nhm.2014.9.683
    [2] Thomas Geert de Jong, Georg Prokert, Alef Edou Sterk . Reaction–diffusion transport into core-shell geometry: Well-posedness and stability of stationary solutions. Networks and Heterogeneous Media, 2025, 20(1): 1-14. doi: 10.3934/nhm.2025001
    [3] Magali Tournus, Aurélie Edwards, Nicolas Seguin, Benoît Perthame . Analysis of a simplified model of the urine concentration mechanism. Networks and Heterogeneous Media, 2012, 7(4): 989-1018. doi: 10.3934/nhm.2012.7.989
    [4] Naoki Sato, Toyohiko Aiki, Yusuke Murase, Ken Shirakawa . A one dimensional free boundary problem for adsorption phenomena. Networks and Heterogeneous Media, 2014, 9(4): 655-668. doi: 10.3934/nhm.2014.9.655
    [5] Leonid Berlyand, Mykhailo Potomkin, Volodymyr Rybalko . Sharp interface limit in a phase field model of cell motility. Networks and Heterogeneous Media, 2017, 12(4): 551-590. doi: 10.3934/nhm.2017023
    [6] Mingming Fan, Jianwen Sun . Positive solutions for the periodic-parabolic problem with large diffusion. Networks and Heterogeneous Media, 2024, 19(3): 1116-1132. doi: 10.3934/nhm.2024049
    [7] Ken-Ichi Nakamura, Toshiko Ogiwara . Periodically growing solutions in a class of strongly monotone semiflows. Networks and Heterogeneous Media, 2012, 7(4): 881-891. doi: 10.3934/nhm.2012.7.881
    [8] Brahim Amaziane, Leonid Pankratov, Andrey Piatnitski . An improved homogenization result for immiscible compressible two-phase flow in porous media. Networks and Heterogeneous Media, 2017, 12(1): 147-171. doi: 10.3934/nhm.2017006
    [9] Bendong Lou . Self-similar solutions in a sector for a quasilinear parabolic equation. Networks and Heterogeneous Media, 2012, 7(4): 857-879. doi: 10.3934/nhm.2012.7.857
    [10] F. R. Guarguaglini, R. Natalini . Nonlinear transmission problems for quasilinear diffusion systems. Networks and Heterogeneous Media, 2007, 2(2): 359-381. doi: 10.3934/nhm.2007.2.359
  • Dynamic recommendation systems aim to achieve real-time updates and dynamic migration of user interests, primarily utilizing user-item interaction sequences with timestamps to capture the dynamic changes in user interests and item attributes. Recent research has mainly centered on two aspects. First, it involves modeling the dynamic interaction relationships between users and items using dynamic graphs. Second, it focuses on mining their long-term and short-term interaction patterns. This is achieved through the joint learning of static and dynamic embeddings for both users and items. Although most existing methods have achieved some success in modeling the historical interaction sequences between users and items, there is still room for improvement, particularly in terms of modeling the long-term dependency structures of dynamic interaction histories and extracting the most relevant delayed interaction patterns. To address this issue, we proposed a Dynamic Context-Aware Recommendation System for dynamic recommendation. Specifically, our model is built on a dynamic graph and utilizes the static embeddings of recent user-item interactions as dynamic context. Additionally, we constructed a Gated Multi-Layer Perceptron encoder to capture the long-term dependency structure in the dynamic interaction history and extract high-level features. Then, we introduced an Attention Pooling network to learn similarity scores between high-level features in the user-item dynamic interaction history. By calculating bidirectional attention weights, we extracted the most relevant delayed interaction patterns from the historical sequence to predict the dynamic embeddings of users and items. Additionally, we proposed a loss function called the Pairwise Cosine Similarity loss for dynamic recommendation to jointly optimize the static and dynamic embeddings of two types of nodes. Finally, extensive experiments on two real-world datasets, LastFM, and the Global Terrorism Database showed that our model achieves consistent improvements over state-of-the-art baselines.



    Recently, the electrical circuit equations as Resistor-Capacitor (RC) and Resistor-Inductor (RL) have attracted many mathematicians [3,15]. The analytical solutions of the electrical circuit equations described by a singular and a no-singular fractional derivative operators have been proposed. In [6], Aguilar et al. have introduced the electrical circuit equations considering the Caputo fractional derivative. They have proposed the analytical solutions of the electrical RC and RL equations described by the Caputo fractional derivative. In [6], the authors have proposed the graphical representations to illustrate the main results. The Caputo fractional derivative is a fractional derivative with a regular kernel. Recently, the fractional derivative operators with no-singular kernels were introduced in the literature. The Caputo-Fabrizio fractional derivative and the Atangana-Baleanu fractional derivative. In [5], Aguilar et al. have introduced electrical equations considering the fractional derivative operators with two parameters. They have proposed the solutions of the electrical RL and RC circuit equations described by the fractional derivative operators with two parameters α,β(0,1]. In [5], the authors have introduced electrical circuit equations using the Atangana-Baleanu fractional derivative. They have proposed the analytical solutions using the Laplace transform. They have presented the numerical scheme of the Atangana-Baleanu fractional derivative of getting the analytical solutions of the electrical RC and RL circuit equations. In [4], Aguilar et al. have presented the electrical circuit equations using the Caputo-Fabrizio fractional derivative. They have proposed the graphical representations of the analytical solutions. In [8], Aguilar et al. have investigated of getting the analytical solution of the RLC circuit equation described by the Caputo fractional derivative. In [7], Aguilar et al. have analyzed and proved the jerk dynamics can be obtained with the electrical circuit equations. There exist many other works related to the analytical solutions, seen in [11,20]. The stability problem interests many mathematicians. The stability problem studies the behaviors of the analytical solutions of the fractional differential equations. The analytical solution of the electrical circuit described by the singular and the no-singular fractional derivative operators is an open problem in fractional calculus and continues to receive many investigations. The analytical solutions of the electrical circuits exist. We can get them. The problem is how to analyze the behaviors of these solutions using stability notions. In [14], the authors have analyzed the stability of the electrical RLC circuit equation. The electrical RL and RC circuit equations contain source terms. The source terms have some properties. The source term of the electrical circuit equations can be constant [10]. The source term can be sinusoidal [5]. They exist some source terms which converge in the times [10]. In stability problems, there exists a novel notion of studying the behaviors the fractional differential equations with these characteristics. This notion is called the fractional input stability recently introduced in the literature by Sene in [18,19]. In this paper, the function containing the source term will be considered as the exogenous input. The contribution of our paper is to recall the analytical solutions of the electrical RL and RC circuit equations described by the Riemann-Liouville and the Caputo fractional derivative using the Laplace transform. Secondly, we will analyze the behaviors of the obtained analytical solutions of the electrical RL and RC circuit equations using the fractional input stability. In other words, we investigate the fractional input stability of the electrical RL and RC circuit equations described by the Riemann-Liouville and the Caputo fractional derivative. The fractional input stability resumes three properties. Firstly, when the considered exogenous input converges, then the analytical solution of the considered electrical circuit converges as well. In other words, a converging input generates a converging state. That is the CICS property of the fractional input stability. Secondly, when the exogenous input is bounded, then the analytical solution of the considered electrical circuit is bounded as well. In other words, a bounded input generates a bounded state. That is the BIBS property of the fractional input stability. And at last, when the the exogenous input is null, then the trivial solution of the unforced electrical circuit is globally asymptotically stable. A fractional differential equation is said to be fractional input stable when the norm of the analytical solution at all times is bounded by a function proportional to the initial state and converging to zero in times plus a positive function depending to the norm of the exogenous input.

    In stability problems in fractional calculus, the asymptotic stability is provided in many manuscripts. Using the fractional input stability, we can study the uniform global asymptotic stability of the fractional differential equations. Presently, the stability conditions for the asymptotic stability were provided in the literature. This paper offers an alternative to study the (uniform) global asymptotic stability of the fractional differential equations. It is the main contribution of this paper. The application of the fractional input stability of the fractional differential equations is the second contribution of this paper.

    The paper is described as the following form. In Section 2, we recall preliminary definitions of the fractional derivative operator and recall the definitions of the stability notions. In Section 3, we investigate the analytical solutions of the electrical RL, RC and LC circuit equations described by the Riemann-Liouville fractional derivative and the Caputo fractional derivative. We analyze the fractional input stability of the obtained analytical solutions. In Section 4, we give the graphical representations of the fractional input stability of the electrical RC and RL circuit equations described by the Caputo fractional derivative operator. In Section 5, we give the conclusions and the remarks.

    Notation: PD denotes the set of all continuous functions χ:R0R0 satisfying χ(0)=0 and χ(s)>0 for all s>0. A class K function is an increasing PD function. The class K denotes the set of all unbounded K function. A continuous function β:R0×R0R0 is said to be class KL if β(.,t)K for any t0 and β(s,.) is non increasing and tends to zero as its arguments tends to infinity. Let xRn, x stands for its Euclidean norm: x:=x21++x2n.

    In this section, we recall the definitions of the fractional derivative operators and the definition of the fractional input stability introduced in the literature [18,19] of fractional calculus. We recall the definition of the Caputo fractional derivative.

    Definition 1. [9,16,17] Consider a function f:[0,+[R, the Caputo fractional derivative of the function f of order α is formulated as the following form

    Dcαf(t)=1Γ(1α)t0f(s)(ts)αds (2.1)

    for all t>0, we consider α(0,1) and Γ(.) is the gamma function.

    The Riemann-Liouville fractional derivative is recalled in the following definition.

    Definition 2. [9,13] Consider a function f:[0,+[R, the Riemann-Liouville fractional derivative of the function f of order α is formulated as the following form

    DRLαf(t)=1Γ(1α)ddtt0f(s)(ts)αds (2.2)

    for all t>0, we consider α(0,1) and Γ(.) is the gamma function.

    The Riemann-Liouville fractional integral is recalled in the following definition.

    Definition 3. [12,18,19] Consider a function f:[0,+[R, the Riemann-Liouville fractional integral of the function f of order α is represented as the following form

    IRLαf(t)=1Γ(1α)t0(ts)α1f(s)ds (2.3)

    for all t>0, we consider α(0,1) and Γ(.) is the gamma function.

    Definition 4. [1,2] The Mittag–Leffler function with two parameters is expressed as the following form

    Eα,β(z)=k=0zkΓ(αk+β) (2.4)

    where α>0,βR and zC.

    We consecrate this section of recalling some stability notions introduced in [18,19]. In general, the fractional differential equation under consideration is defined by

    Dcαx=f(t,x,u) (2.5)

    where xRn, a continuous and locally Lipschitz function f:R+×Rn×RmRn and uRm represents the exogenous input. Note that the solution of the fractional differential equation (2.5) exists under the assumption "the function f is continuous and locally Lipschitz". The existence of the solution is fundamental in the stability problems. It is not necessary to study the stability of the fractional differential equation when we are not sure its the solution exists.

    Definition 5. [18,19] The fractional differential equation (2.5) is fractional input stable if for any exogenous input uRm, there exists a function βKL, a function γK such that for any initial state x0 the solution of (2.5) satisfies

    x(t,x0,u)β(x0,t)+γ(u). (2.6)

    The function γ is a class K function and the function β is a class KL. Thus, from equation (2.6), when the exogenous input u of the fractional differential equation (2.5) is bounded it follows its solution is bounded as well, that is the BIBS property. See the proof in the next section or in the original paper [18]. We can also notice, when the exogenous input of the fractional differential equation (2.5) converges, when t tends to infinity, then its solution converges as well, that is CICS property, see in [18] for more details. At last, we observe from equation (2.6) when the exogenous input into the fractional differential equation (2.5) is null; we recover the definition of the global asymptotic stability described in the following definition.

    Definition 6. [16] The trivial solution of the unforced fractional differential equation defined by Dcαx=f(t,x,0) is said to be (uniform) globally asymptotically stable if there exist a function βKL, such that for any initial condition x0 the solution of Dcαx=f(t,x,0) satisfies

    x(t,x0,0)β(x0,t). (2.7)

    The last remark is the main contribution of the fractional input stability of the fractional differential equations. We finish this section by recalling the following lemma. It will be used to introduce comparison functions.

    Lemma 1. [21] Let α(0,2), β is a real number, a matrix ACn×n, μ is such that πα2<μ<min{π,πα} and C>0 is a real, then it holds that

    Eα,β(A)C1+A (2.8)

    where μarg(λ(A))π, λ(A) represents the eigenvalues of the matrix A.

    In this section, we address the fractional input stability of the electrical circuit equations described by the Riemann-Liouville and the Caputo fractional derivative operators. The electrical circuit equations under consideration are the electrical RL circuit equation, the electrical LC circuit equation, and the electrical RC circuit equation. We analyze the bounded input bounded state (BIBS), the converging input converging state (CICS) and the (uniform) global asymptotic stability of the unforced fractional electrical circuit equations, properties derived to the fractional input stability of the electrical circuit equations. It is important to note, in the rest of the paper, the second term of all the fractional electrical circuit equations is considered as a single function which represents the exogenous input.

    In this section, we address the fractional input stability of the electrical RL circuit described by the Riemann-Liouville fractional derivative. We consider the electrical RL circuit represented by the following fractional differential equation

    DαRLI(t)+σ1αRLI(t)=u(t) (3.1)

    with the initial boundary condition defined by (I1αI)(a)=I0. I represents the current across the inductor. The parameter σ is associated with the temporal components in the system, see more details in [5,6]. R represents the resistance, and L represents the inductance. In the second member, u(t) is considered as the exogenous input. It contains the source term E(t) (eventually depend on time), the function 1/L and the temporal coefficient σ1α. Summarizing u(t)=σ1αE(t)L.

    Theorem 1. The electrical RL circuit defined by equation (3.1) described by the Riemann-Liouville fractional derivative is fractional input stable.

    Proof: We first determine the analytical solution of the fractional differential equation defined by equation (3.1). Applying the Laplace transform to both sides of equation (3.1), we obtain the following relationships

    sαL(I(t))(I1αI)(0)+σ1αRLL(I(t))=L(u(t))sαL(I(t))I0+σ1αRLL(I(t))=L(u(t))L(I(t)){sα+σ1αRL}=I0+L(u(t))L(I(t))=I0sα+σ1αRL+L(u(t))sα+σ1αRLL(I(t))=I0L(tα1Eα,α(σ1αRLtα))+L(tα1Eα,α(σ1αRLtα))L(u(t))L(I(t))=I0L(tα1Eα,α(σ1αRLtα))+L(tα1Eα,α(σ1αRLtα)u(t)) (3.2)

    where L represents the usual Laplace transform and "" represents the usual convolution product. Applying the inverse of Laplace transform to both sides of equation (3.2), we obtain the following analytical solution

    I(t)=I0tα1Eα,α(σ1αRLtα)+t0(ts)α1Eα,α(σ1αRL(ts)α)u(s)ds. (3.3)

    From which it follows by applying the euclidean norm

    I(t)I0tα1Eα,α(σ1αRLtα)+ut0(ts)α1Eα,α(σ1αRL(ts)α)ds. (3.4)

    We know the term σ1αRL0, then there exists a positive constant M such that we have the following inequality [17]

    t0(ts)α1Eα,α(σ1αRL(ts)α)dsM. (3.5)

    Using Lemma 1 and inequality (3.5), we obtain the following inequality

    I(t)I0C1t1α+σ1αRLt+uM. (3.6)

    Let the function β(I0,t)=I0C1t1α+σ1αRLt. We observe the function β(.,t) is a class K function. Furthermore, β(s,.) decays with the time and converges to zero when t tends to infinity, thus β(s,.) is a class L function. In conclusion the function β is a class KL function. Let the function γ(u)=uM, it is straightforward to see γ is a class K function. Finally, equation (3.6) is represented as follows

    I(t)β(I0,t)+γ(u). (3.7)

    It follows from equation (3.7) the electrical RL circuit described by the Riemann-Liouville fractional derivative is fractional input stable.

    Let the exogenous input u=0 then γ(0)=0 (because γ is a class K function), thus equation (3.7) becomes

    I(t)β(I0,t). (3.8)

    Then the equilibrium point I=0 of the electrical RL circuit defined by

    DαRLI(t)+σ1αRLI(t)=0 (3.9)

    is (uniformly) globally asymptotically stable. Then, the fractional input stability of the electrical RL circuit equation (3.1) implies the global asymptotic stability of the unforced electrical RL circuit equation (3.9). Proving the global asymptotic stability of the equilibrium point I=0 of the unforced electrical RL circuit equation (3.9) using the fractional input stability is essential. The classical tools give the asymptotic stability of the equilibrium point I=0. Its don't give the global asymptotic stability. Thus, the fractional input stability is a good compromise in the stability problems. We observe when the exogenous input of equation (3.1) converges; then from equation (3.7), we notice the analytical solution of the electrical RL circuit equation (3.1) converges as well. That is the CICS property. Furthermore, from equation when the exogenous input equation (3.1) is bounded, then the analytical solution of the electrical RL circuit equation (3.1) is bounded as well. We give the proof. Let the exogenous input uη then from the fact γ is a class K, there exists ϵ such that γ(u)ϵ. Then equation (3.7) becomes

    I(t)β(I0,t)+ϵβ(I0,0)+ϵ. (3.10)

    Thus the analytical solution is bounded as well.

    In this section, we replace the Riemann-Liouville fractional derivative by the Caputo fractional derivative. We address the fractional input stability of the electrical RL circuit described by the Caputo fractional derivative. The following fractional differential equation defines the electrical RL circuit equation described by the Caputo fractional derivative

    DαcI(t)+σ1αRLI(t)=u(t) (3.11)

    with the initial boundary condition defined by I(0)=I0. The term u(t) represents the exogenous input as defined in the previous section.

    Theorem 2. The electrical RL circuit defined by equation (3.11) described by the Caputo fractional derivative is fractional input stable.

    Proof: We apply the Laplace transform to both sides of equation (3.11), we obtain the following relationships

    sαL(I(t))sα1I(0)+σ1αRLL(I(t))=L(u(t))sαL(I(t))sα1I0+σ1αRLL(I(t))=L(u(t))L(I(t)){sα+σ1αRL}=sα1I0+L(u(t))L(I(t))=sα1I0sα+σ1αRL+L(u(t))sα+σ1αRLL(I(t))=I0L(Eα(σ1αRLtα))+L(tα1Eα,α(σ1αRLtα))L(u(t))L(I(t))=I0L(Eα(σ1αRLtα))+L(tα1Eα,α(σ1αRLtα)u(t)) (3.12)

    where L represents the usual Laplace transform and "" represents the usual convolution product. We apply the inverse of Laplace transform to both sides of equation ((3.12). We obtain the following analytical solution

    I(t)=I0Eα(σ1αRLtα)+t0(ts)α1Eα,α(σ1αRL(ts)α)u(s)ds. (3.13)

    We apply the usual euclidean norm to equation ((3.13). It follows the following inequality

    I(t)I0Eα(σ1αRLtα)+ut0(ts)α1Eα,α(σ1αRL(ts)α)ds (3.14)

    The characteristic term σ1αRL0, then there exists a positive constant M such that the following inequality is held

    t0(ts)α1Eα,α(σ1αRL(ts)α)dsM (3.15)

    Using Lemma 1 and the inequality defined by equation (3.15), we obtain the following inequality

    I(t)I0C11+σ1αRLtα+uM (3.16)

    Let the function β(I0,t)=I0C11+σ1αRLtα. We observe the function β(.,t) is a class K function. Furthermore, β(s,.) decays in time and converges to zero when t tends to infinity, thus the function β(s,.) is a class L function. In conclusion the function β is a class KL function. Let the function γ(u)=uM. The function γ is a class K function. Finally, equation (3.16) is represented as the following form

    I(t)β(I0,t)+γ(u) (3.17)

    It follows from equation (3.17) the electrical RL circuit equation described by the Caputo fractional derivative is fractional input stable.

    Let the exogenous input u=0. From the fact γ is a class K function, we have γ(0)=0. Equation (3.17) is represented as the following form

    I(t)β(I0,t) (3.18)

    From equation (3.18), the equilibrium point I=0 of the electrical RL circuit equation described by the Caputo fractional derivative defined by

    DαcI(t)+σ1αRLI(t)=0 (3.19)

    is globally asymptotically stable. Then, the fractional input stability of equation (3.11) implies the global asymptotic stability of the unforced electrical RL circuit equation (3.19). We observe when the exogenous input of equation (3.11) converges, then from equation (3.17), we notice the analytical solution of the electrical RL circuit equation (3.11) converges as well. From equation (3.17), the analytical solution of the electrical RL circuit equation (3.11) described by the Caputo fractional derivative is bounded as well when the exogenous input is bounded.

    In this section, we study the fractional input stability of the electrical LC circuit described by the Riemann-Liouville fractional derivative. The electrical LC circuit equation under consideration is defined by the following fractional differential equation

    D2αRLI(t)+σ1αLCI(t)=u(t) (3.20)

    with the initial boundary condition defined by (I12αI)(0)=I0 and α1/2 (in this paper). I represents the current across the inductor. The parameter σ is associated with the temporal components in the system, see more details in [6]. In the second member, u(t) represents the exogenous input. It is expressed using the source term E(t) (eventually depend on time), the function C/LC and the temporal coefficient σ1α. Summarizing, we have u(t)=σ1αCE(t)LC. We make the following theorem.

    Theorem 3. The electrical LC circuit equation defined by equation (3.20) described by the Riemann-Liouville fractional derivative is fractional input stable.

    Proof: Let β=2α. We apply the Laplace transform to both sides of equation (3.20), we obtain the following relationships

    sβL(I(t))(I1βI)(0)+σ1αLCL(I(t))=L(u(t))sβL(I(t))I0+σ1αLCL(I(t))=L(u(t))L(I(t)){sβ+σ1αLC}=I0+L(u(t))L(I(t))=I0sβ+σ1αLC+L(u(t))sβ+σ1αLCL(I(t))=I0L(tβ1Eβ,β(σ1αLCtβ))+L(tβ1Eβ,β(σ1αLCtβ))L(u(t))L(I(t))=I0L(tβ1Eβ,β(σ1αLCtβ))+L(tβ1Eβ,β(σ1αLCtβ)u(t)) (3.21)

    where L represents the usual Laplace transform and "" represents the usual convolution product. We apply the inverse of Laplace transform to both sides of equation (3.21). We obtain the following analytical solution

    I(t)=I0tβ1Eβ,β(σ1αLCtβ)+t0(ts)β1Eβ,β(σ1αLC(ts)β)u(s)ds (3.22)

    We apply the euclidean norm to both sides of equation (3.22). We get the following relationship

    I(t)I0tβ1Eβ,β(σ1αLCtβ)+ut0(ts)β1Eβ,β(σ1αLC(ts)β)ds (3.23)

    From the assumption σ1αLC0, there exists a positive constant M such that we have the following inequality.

    t0(ts)β1Eβ,β(σ1αLC(ts)β)dsM (3.24)

    We use Lemma 1 and inequality (3.23), we obtain the following inequality

    I(t)I0C1t1β+σ1αLCt+uM. (3.25)

    Let the function μ(I0,t)=I0C1t1β+σ1αLCt. We observe the function μ is a class KL function. Let the function γ(u)=uM, it is straightforward to verify the function γ is a class K function. Finally, equation (3.25) is represented as follows

    I(t)μ(I0,t)+γ(u) (3.26)

    It follows from equation (3.26) the electrical LC circuit described by the Riemann-Liouville fractional derivative is fractional input stable.

    Let the exogenous input u=0. From the fact γ(0)=0, equation (3.26) becomes

    I(t)μ(I0,t). (3.27)

    Thus the equilibrium point I=0 of the electrical LC circuit defined by

    D2αRLI(t)+σ1αLCI(t)=0 (3.28)

    is globally asymptotically stable. As in the previous sections, the fractional input stability of equation (3.20) implies the global asymptotic stability of the unforced electrical LC circuit equation (3.28). We observe when the exogenous input of equation (3.20) converges, it follows from equation (3.26), the analytical solution of the electrical LC circuit equation (3.20) converges as well. It follows from equation (3.26), the analytical solution of the electrical LC circuit equation (3.20) is bounded as well when its exogenous input of equation is bounded.

    We consider the Caputo fractional derivative in this section. We investigate the fractional input stability of the electrical LC circuit equation defined by the following fractional differential equation

    D2αcI(t)+σ1αLCI(t)=u(t) (3.29)

    with the initial boundary condition defined by I(0)=I0 and α1/2. The exogenous input is u(t).

    Theorem 4. The electrical LC circuit equation defined by equation (3.29) described by the Caputo fractional derivative is fractional input stable.

    Proof: Let β=2α. We determine the analytical solution of the fractional differential equation defined by (3.29). We apply the Laplace transform to both sides of equation (3.29). We obtain the following relationships

    sβL(I(t))sβ1I(0)+σ1αLCL(I(t))=L(u(t))sβL(I(t))sβ1I0+σ1αLCL(I(t))=L(u(t))L(I(t)){sβ+σ1αLC}=sβ1I0+L(u(t))L(I(t))=sβ1I0sβ+σ1αLC+L(u(t))sβ+σ1αLCL(I(t))=I0L(Eβ(σ1αLCtβ))+L(tβ1Eβ,β(σ1αLCtβ))L(u(t))L(I(t))=I0L(Eβ(σ1αLCtβ))+L(tβ1Eβ,β(σ1αLCtβ)u(t)) (3.30)

    where L represents the usual Laplace transformation and "" represents the usual convolution product. We apply the inverse of Laplace transform to both sides of equation (3.30). We obtain the following analytical solution

    I(t)=I0Eβ(σ1αLCtβ)+t0(ts)β1Eβ,β(σ1αLC(ts)β)u(s)ds (3.31)

    We apply the euclidean norm to both sides of equation (3.31). We obtain the following inequality

    I(t)I0Eβ(σ1αLCtβ)+ut0(ts)β1Eβ,β(σ1αLC(ts)β)ds (3.32)

    From the assumption σ1αLC0, there exists a positive constant M such that the following inequality is held

    t0(ts)β1Eβ,β(σ1αLC(ts)β)dsM (3.33)

    We use Lemma 1 and equation (3.33), we obtain the following inequality

    I(t)I0C11+σ1αLCtβ+uM (3.34)

    Let the function μ(I0,t)=I0C11+σ1αLCtβ. We observe the function μ(.,t) is a class K function. Furthermore, μ(s,.) decays in time and converges to zero when t tends to infinity. Thus μ(s,.) is a class L function. In conclusion the function μ is a class KL function. Let the function γ(u)=uM, it is straightforward to see γ is a class K function. Finally equation (3.34) is represented as follows

    I(t)μ(I0,t)+γ(u) (3.35)

    It follows from equation (3.35) the electrical LC circuit equation (3.29) described by the Caputo fractional derivative is fractional input stable.

    Let's analyze the BIBS, the CICS properties and the global asymptotic stability of the unforced electrical LC circuit equation. Let the exogenous input u=0. From the fact γ is a class K function, we have γ(0)=0. Then the equation (3.35) becomes

    I(t)μ(I0,t) (3.36)

    That is the trivial solution of the electrical LC circuit equation described by the Caputo fractional derivative defined by

    D2αcI(t)+σ1αLCI(t)=0 (3.37)

    is globally asymptotically stable. That is to say the fractional input stability implies the global asymptotic stability of the unforced electrical RL circuit described by the Caputo fractional derivative. From equation (3.35), when the exogenous input of equation (3.29) converges then the analytical solution of the electrical LC circuit equation (3.29) converges as well. We notice from equation (3.35), when the exogenous input of equation (3.29) is bounded, then the analytical solution of the electrical LC circuit equation (3.29) described by the Caputo fractional derivative is bounded as well.

    In this section, we investigate the fractional input stability of the electrical RC circuit equation described by the Riemann-Liouville fractional derivative. Let the electrical RC circuit equation defined by the following fractional differential equation

    DαRLV(t)+σ1αRCV(t)=u(t) (3.38)

    with the initial boundary condition defined by (I1αV)(0)=V0. C represents the capacitance and R represents the resistance. V represents the voltage across the capacitor. The parameter σ is associated to the temporal components in the system, see more details in [5]. The second member, u(t) is considered as the exogenous input. It contains the source term E(t) (eventually depend on time), the function 1/RC and the temporal coefficient σ1α. Summarizing u(t)=σ1αE(t)RC. We make the following theorem.

    Theorem 5. The electrical RC circuit defined by equation (3.38) described by the Riemann-Liouville fractional derivative is fractional input stable.

    Proof: Applying the Laplace transform to both sides of equation (3.38), we get the following relationships

    sαL(V(t))(I1αV)(0)+σ1αRCL(V(t))=L(u(t))sαL(V(t))V0+σ1αRCL(V(t))=L(u(t))L(V(t)){sα+σ1αRC}=V0+L(u(t))L(V(t))=V0sα+σ1αRC+L(u(t))sα+σ1αRCL(V(t))=I0L(tα1Eα,α(σ1αRCtα))+L(tα1Eα,α(σ1αRCtα))L(u(t))L(V(t))=I0L(tα1Eα,α(σ1αRCtα))+L(tα1Eα,α(σ1αRCtα)u(t)) (3.39)

    where L represents the usual Laplace transform and "" represents the usual convolution product. Applying the inverse of Laplace transform to both sides of equation (3.39), we obtain the following analytical solution

    V(t)=V0tα1Eα,α(σ1αRCtα)+t0(ts)α1Eα,α(σ1αRC(ts)α)u(s)ds (3.40)

    Applying the euclidean norm on equation (3.40), we have

    V(t)V0tα1Eα,α(σ1αRCtα)+ut0(ts)α1Eα,α(σ1αRC(ts)α)ds (3.41)

    From the assumption σ1αRC0, there exists a positive constant M such that we have the following inequality

    t0(ts)α1Eα,α(σ1αRC(ts)α)dsM (3.42)

    We use Lemma 1 and the inequality (3.42), we obtain the following inequality

    V(t)V0C1t1α+σ1αRCt+uM (3.43)

    Let the function β(V0,t)=V0C1t1α+σ1αRCt. We observe the function β is a class KL function. Let the function γ(u)=uM. It is straightforward to see γ is a class K function. Finally, equation (3.43) is represented as the following form

    V(t)β(V0,t)+γ(u) (3.44)

    It follows from equation (3.44) the electrical RC circuit equation described by the Riemann-Liouville fractional derivative is fractional input stable.

    Let the exogenous input u=0, then we have γ(0)=0. Thus the equation (3.44) becomes

    V(t)β(I0,t) (3.45)

    Then the equilibrium point V=0 of the electrical RC circuit equation defined by

    DαRLV(t)+σ1αRCV(t)=0 (3.46)

    is (uniformly) globally asymptotically stable. Thus the fractional input stability implies the global asymptotic stability of the unforced electrical RC circuit described by the Riemann-Liouville fractional derivative. We observe from (3.44) when the exogenous input of equation (3.38) converges, we notice the analytical solution of the electrical RC circuit equation (3.38) converges as well. From (3.44), when the exogenous input term of equation (3.38) is bounded, then the analytical solution of the electrical RC circuit equation (3.38) is bounded as well.

    In this section, we investigate the fractional input stability of the electrical RC circuit equation described by the Caputo fractional derivative defined by the following fractional differential equation

    DαcV(t)+σ1αRCV(t)=u(t) (3.47)

    with the initial boundary condition defined by V(0)=V0. The term u(t) represents the exogenous input as defined in the previous section.

    Theorem 6. The electrical RC circuit defined by equation (3.47) described by the Caputo fractional derivative is fractional input stable.

    Proof: Applying the Laplace transform to both sides of equation (3.47), we obtain the following relationships

    sαL(V(t))sα1V(0)+σ1αRCL(V(t))=L(u(t))sαL(V(t))sα1V0+σ1αRCL(V(t))=L(u(t))L(V(t)){sα+σ1αRC}=sα1V0+L(u(t))L(V(t))=sα1V0sα+σ1αRC+L(u(t))sα+σ1αRCL(V(t))=V0L(Eα(σ1αRCtα))+L(tα1Eα,α(σ1αRCtα))L(u(t))L(V(t))=V0L(Eα(σ1αRCtα))+L(tα1Eα,α(σ1αRCtα)u(t)) (3.48)

    where L represents the usual Laplace transform and "" represents the usual convolution product. Applying the inverse of Laplace transform to both sides of equation (3.48), we obtain the following analytical solution

    V(t)=V0Eα(σ1αRCtα)+t0(ts)α1Eα,α(σ1αRC(ts)α)u(s)ds (3.49)

    From which we have the following inequality

    V(t)V0Eα(σ1αRCtα)+ut0(ts)α1Eα,α(σ1αRC(ts)α)ds (3.50)

    From the assumption σ1αRC0, there exists a positive constant M such that the following inequality is held

    t0(ts)α1Eα,α(σ1αRC(ts)α)dsM (3.51)

    We use Lemma 1 and equation (3.51), we obtain the following relationships

    V(t)V0C11+σ1αRCtα+uM (3.52)

    Let the function β(V0,t)=V0C11+σ1αRCtα. We observe the function β is a class KL function. Let the function γ(u)=uM. It is straightforward to see γ is a class K function. Finally, equation (3.52) is represented as follows

    V(t)β(V0,t)+γ(u) (3.53)

    From equation (3.53) the electrical RC circuit equation (3.47) described by the Caputo fractional derivative is fractional input stable.

    Let the exogenous input u=0. From the fact γ is a class K function, we have γ(0)=0. Then equation (3.53) becomes

    V(t)β(V0,t) (3.54)

    We conclude the equilibrium point V=0 of the electrical RC circuit equation described by the Caputo fractional derivative defined by

    DαcV(t)+σ1αRCV(t)=0 (3.55)

    is globally asymptotically stable. We observe when the input term of equation (3.47) converges; then from equation (3.53), we notice the analytical solution of the electrical RC circuit equation (3.47) described by the Caputo fractional derivative converges as well. We notice when the exogenous input of equation (3.47) is bounded, then the analytical solution of the electrical RC circuit equation (3.47) described by the Caputo fractional derivative is bounded as well.

    In this section, we analyze the CICS and the global asymptotic stability properties obtained with the fractional input stability. Let's the electrical RL circuit equation described by the Caputo fractional derivative defined by

    DαcI(t)+σ1αRLI(t)=u(t) (4.1)

    with numerical values: the resistance R=10Ω and induction L=10H. Let's the exogenous input u(t)=0. The current across the inductor is depicted in Figure 1. We observe all the analytical solutions I decay everywhere except at the equilibrium point itself, thus the equilibrium point I=0 of the electrical RL circuit equation (4.1) is (uniformly) globally asymptotically stable.

    Figure 1.  I=0 the electrical RL circuit equation (4.1) is globally asymptotically stable.

    In Figure 2, we observe the behavior of the current in the inductor when the electrical RL circuit equation is fractional input stable.

    Figure 2.  Fractional input stability of the electrical RL circuit equation.

    Let's the electrical RC circuit equation described by the Caputo fractional derivative with numerical values defined by

    DαcV(t)+σ1αRCV(t)=u(t) (4.2)

    with the resistance R=10kΩ and the capacitance C=1000μF. Let's the exogenous input u(t)=0, we can, observe all the analytical solutions V decay everywhere except at the equilibrium point itself, thus the equilibrium point V=0 of the electrical RL circuit equation (4.2) is globally asymptotically stable, see figure 3.

    Figure 3.  V=0 of the electrical RC circuit equation is globally asymptotically stable.

    In Figure 4, we observe the behavior of the analytical solution of the electrical RC circuit equation when it is fractional input stability.

    Figure 4.  Fractional input stability of the electrical RC circuit.

    In this paper, we have discussed the fractional input stability of the electrical circuit equation described by the Riemann-Liouville and the Caputo fractional derivative operators. This paper is the application of the fractional input stability in the electrical circuit equations. And we have noticed the fractional input stability is an excellent compromise to study the behavior of the analytical solution of the electrical RL, RC and LC circuit equations.

    The author declare that there is no conflict of interest.



    [1] Z. Lin, An empirical investigation of user and system recommendations in e-commerce, Decis. Support Syst., 68 (2014), 111–124. https://doi.org/10.1016/j.dss.2014.10.003 doi: 10.1016/j.dss.2014.10.003
    [2] T. Iba, K. Nemoto, B. Peters, P. A. Gloor, Analyzing the creative editing behavior of wikipedia editors: Through dynamic social network analysis, Proc.-Soc. Behav. Sci., 2 (2010), 6441–6456. https://doi.org/10.1016/j.sbspro.2010.04.054 doi: 10.1016/j.sbspro.2010.04.054
    [3] T. R. Liyanagunawardena, A. A. Adams, S. A. Williams, MOOCs: A systematic study of the published literature 2008–2012, Int. Rev. Res. Open Distrib. Learn., 14 (2013), 202–227. https://doi.org/10.19173/irrodl.v14i3.1455 doi: 10.19173/irrodl.v14i3.1455
    [4] Q. Liu, S. Wu, L. Wang, T. Tan, Predicting the next location: A recurrent model with spatial and temporal contexts, in Thirtieth AAAI Conference on Artificial Intelligence, 30 (2016). https://doi.org/10.1609/aaai.v30i1.9971
    [5] S. Wu, Q. Liu, P. Bai, L. Wang, T. Tan, SAPE: A system for situation-aware public security evaluation, in Thirtieth AAAI Conference on Artificial Intelligence, 30 (2016). https://doi.org/10.1609/aaai.v30i1.9828
    [6] Z. Hou, X. Lv, Y. Zhou, L. Bu, Q. Ma, Y. Wang, A dynamic graph Hawkes process based on linear complexity self-attention for dynamic recommender systems, PeerJ. Comput. Sci., 9 (2023), 1368. https://doi.org/10.7717/peerj-cs.1368 doi: 10.7717/peerj-cs.1368
    [7] Y. Zheng, X. Yi, M. Li, R. Li, Z. Shan, E. Chang, et al., Forecasting fine-grained air quality based on big data, in 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2015), 2267–2276. https://doi.org/10.1145/2783258.2788573
    [8] X. Wang, X. He, M. Wang, F. Feng, T. Chua, Neural graph collaborative filtering, in 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, (2019), 165–174. https://doi.org/10.1145/3331184.3331267
    [9] X. He, K. Deng, X. Wang, Yan Li, Y. Zhang, M. Wang, LightGCN: Simplifying and powering graph convolution network for recommendation, in 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, (2020), 639–648. https://doi.org/10.1145/3397271.3401063
    [10] Y. Koren, R. Bell, C. Volinsky, Matrix factorization techniques for recommender systems, Computer, 42 (2009), 30–37. https://doi.org/10.1109/MC.2009.263 doi: 10.1109/MC.2009.263
    [11] D. Lee, H. S. Seung, Algorithms for non-negative matrix factorization, in 13th International Conference on Neural Information Processing Systems, (2000), 535–541.
    [12] R. Salakhutdinov, A. Mnih, Probabilistic matrix factorization, in 20th International Conference on Neural Information Processing Systems, (2007), 1257–1264.
    [13] S. Raza, C. Ding, Progress in context-aware recommender systems-An overview, Comput. Sci. Rev., 31 (2019), 84–97. https://doi.org/10.1016/j.cosrev.2019.01.001 doi: 10.1016/j.cosrev.2019.01.001
    [14] G. Adomavicius, K. Bauman, A. Tuzhilin, M. Unger, Context-aware recommender systems: From foundations to recent developments, in Recommender Systems Handbook, (2022), 211–250. https://doi.org/10.1007/978-1-0716-2197-4_6
    [15] C. Wu, A. Ahmed, A. Beutel, A. J. Smola, H. Jing, Recurrent recommender networks, in Tenth ACM International Conference on Web Search and Data Mining, (2017), 495–503. https://doi.org/10.1145/3018661.3018689
    [16] S. Kumar, X. Zhang, J. Leskovec, Predicting dynamic embedding trajectory in temporal interaction networks, in 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (2019), 1269–1278. https://doi.org/10.1145/3292500.3330895
    [17] X. Li, M. Zhang, S. Wu, Z. Liu, L. Wang, P. S. Yu, Dynamic graph collaborative filtering, in 2020 IEEE International Conference on Data Mining, (2020), 322–331. https://doi.org/10.1109/ICDM50108.2020.00041
    [18] Z. Kefato, S. Girdzijauskas, N. Sheikh, A. Montresor, Dynamic embeddings for interaction prediction, in Web Conference 2021, (2021), 1609–1618. https://doi.org/10.1145/3442381.3450020
    [19] S. Wu, F. Sun, W. Zhang, X. Xie, B. Cui, Graph neural networks in recommender systems: A survey, ACM Comput. Surv., 55 (2022), 1–37. https://doi.org/10.1145/3535101 doi: 10.1145/3535101
    [20] P. Covington, J. Adams, E. Sargin, Deep neural networks for YouTube recommendations, in 10th ACM Conference on Recommender Systems, (2016), 191–198. https://doi.org/10.1145/2959100.2959190
    [21] H. Dai, Y. Wang, R. Trivedi, L. Song, Recurrent coevolutionary latent feature processes for continuous-time recommendation, in 1st Workshop on Deep Learning for Recommender Systems, (2016), 29–34. https://doi.org/10.1145/2988450.2988451
    [22] Y. K. Tan, X. Xu, Y. Liu, Improved recurrent neural networks for session-based recommendations, in 1st Workshop on Deep Learning for Recommender Systems, (2016), 17–22. https://doi.org/10.1145/2988450.2988452
    [23] Z. Wang, W. Wei, G. Cong, X. Li, X. Mao, M. Qiu, Global context enhanced graph neural networks for session-based recommendation, in 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, (2020), 169–178. https://doi.org/10.1145/3397271.3401142
    [24] S. Wu, Y. Tang, Y. Zhu, L. Wang, X. Xie, T. Tan, Session-based recommendation with graph neural networks, in AAAI Technical Track: AI and the Web, 33 (2019), 346–353. https://doi.org/10.1609/aaai.v33i01.3301346
    [25] C. Xu, P. Zhao, Y. Liu, V. S. Sheng, J. Xu, F. Zhuang, Graph contextualized self-attention network for session-based recommendation, in Twenty-Eighth International Joint Conference on Artificial Intelligence, (2019), 3940–3946. https://doi.org/10.24963/ijcai.2019/547
    [26] B. Hidasi, A. Karatzoglou, L. Baltrunas, D. Tikk, Session-based recommendations with recurrent neural networks, preprint, arXiv: 1511.06939.
    [27] J. Li, P. Ren, Z. Chen, Z. Ren, T. Lian, J. Ma, Neural attentive session-based recommendation, in 2017 ACM on Conference on Information and Knowledge Management, (2017), 1419–1428. https://doi.org/10.1145/3132847.3132926
    [28] Q. Liu, S. Wu, D. Wang, Z. Li, L. Wang, Context-aware sequential recommendation, in 2016 IEEE 16th International Conference on Data Mining (ICDM), (2016), 1053–1058. https://doi.org/10.1109/ICDM.2016.0135
    [29] J. You, Y. Wang, A. Pal, P. Eksombatchai, C. Rosenburg, Hierarchical temporal convolutional networks for dynamic recommender systems, in The World Wide Web Conference, (2019), 2236–2246. https://doi.org/10.1145/3308558.3313747
    [30] Y. Wang, N. Du, R. Trivedi, L. Song, Coevolutionary latent feature processes for continuous-time user-item interactions, in 30th Conference on Neural Information Processing Systems, (2016), 1–9.
    [31] Q. Wu, Y. Gao, X. Gao, P. Weng, G. Chen, Dual sequential prediction models linking sequential recommendation and information dissemination, in 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (2019), 447–457. https://doi.org/10.1145/3292500.3330959
    [32] R. Trivedi, M. Farajtabar, P. Biswal, H. Zha, DyRep: Learning representations over dynamic graphs, in International Conference on Learning Representations 2019 Conference, (2019).
    [33] A. Beutel, P. Covington, S. Jain, C. Xu, J. Li, V. Gatto, et al., Latent cross: Making use of context in recurrent recommender systems, in Eleventh ACM International Conference on Web Search and Data Mining, (2018), 46–54. https://doi.org/10.1145/3159652.3159727
    [34] Y. Zhu, H. Li, Y. Liao, B. Wang, Z. Guan, H. Liu, et al., What to do next: Modeling user behaviors by time-LSTM, in 26th International Joint Conference on Artificial Intelligence, (2017), 3602–3608.
    [35] Y. Zhang, X. Yang, J. Ivy, M. Chi, ATTAIN: Attention-based time-aware LSTM networks for disease progression modeling, in Twenty-Eighth International Joint Conference on Artificial Intelligence, (2019), 4369–4375. https://doi.org/10.24963/ijcai.2019/607
    [36] W. Kang, J. McAuley, Self-attentive sequential recommendation, in 2018 IEEE International Conference on Data Mining, (2018), 197–206. https://doi.org/10.1109/ICDM.2018.00035
    [37] D. Hendrycks, K. Gimpel, Gaussian error linear units (GeLUs), preprint, arXiv: 1606.08415.
    [38] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L. Chen, MobileNetV2: Inverted residuals and linear bottlenecks, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), 4510–4520. https://doi.org/10.1109/CVPR.2018.00474
    [39] Y. N. Dauphin, A. Fan, M. Auli, D. Grangier, Language modeling with gated convolutional networks, in 34th International Conference on Machine Learning, 70 (2017), 933–941.
    [40] R. K. Srivastava, K. Greff, J. Schmidhuber, Highway networks, preprint, arXiv: 1505.00387.
    [41] S. Hochreiter, J. Schmidhuber, Long Short-Term Memory, Neural Comput., 9 (1997), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735 doi: 10.1162/neco.1997.9.8.1735
    [42] J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), 7132–7141. https://doi.org/10.1109/CVPR.2018.00745
    [43] C. Santos, M. Tan, B. Xiang, B. Zhou, Attentive pooling networks, preprint, arXiv: 1602.03609.
    [44] R. Hadsell, S. Chopra, Y. LeCun, Dimensionality reduction by learning an invariant mapping, in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), (2006), 1735–1742. https://doi.org/10.1109/CVPR.2006.100
    [45] S. Yang, W. Yu, Y. Zheng, H. Yao, T. Mei, Adaptive semantic-visual tree for hierarchical embeddings, in 27th ACM International Conference on Multimedia, (2019), 2097–2105. https://doi.org/10.1145/3343031.3350995
    [46] X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, M. Wang, Lightgcn: Simplifying and powering graph convolution network for recommendation, in 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, (2020), 639–648. https://doi.org/10.1145/3397271.3401063
    [47] C. Hsieh, L. Yang, Y. Cui, T. Lin, S. Belongie, D. Estrin, Collaborative metric learning, in 26th International Conference on World Wide Web, (2017), 193–201. https://doi.org/10.1145/3038912.3052639
    [48] B. Fu, W. Zhang, G. Hu, X. Dai, S. Huang, J. Chen, Dual side deep context-aware modulation for social recommendation, in Web Conference 2021, (2021), 2524–2534. https://doi.org/10.1145/3442381.3449940
    [49] T. Mikolov, I. Sutskever, K. Chen, G. Corrado, J. Dean, Distributed representations of words and phrases and their compositionality, in 26th International Conference on Neural Information Processing Systems, 2 (2013), 3111–3119.
    [50] R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in 2014 IEEE Conference on Computer Vision and Pattern Recognition, (2014), 580–587. https://doi.org/10.1109/CVPR.2014.81
    [51] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15 (2014), 1929–1958.
    [52] B. Hidasi, D. Tikk, Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback, in 2012th European Conference on Machine Learning and Knowledge Discovery in Databases, (2012), 67–82.
    [53] X. Glorot, Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, in 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, (2010), 249–256.
    [54] J. Chung, C. Gulcehre, K. Cho, Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling, preprint, arXiv: 1412.3555.
    [55] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., Attention is all you need, in 31st International Conference on Neural Information Processing Systems, (2017), 6000–6010.
    [56] T. Silveira, M. Zhang, X. Lin, Y. Liu, S. Ma, How good your recommender system is? A survey on evaluations in recommendation, Int. J. Mach. Learn. Cybern., 10 (2019), 813–831. https://doi.org/10.1007/s13042-017-0762-9 doi: 10.1007/s13042-017-0762-9
  • This article has been cited by:

    1. Bin Chen, Sergey A. Timoshin, Optimal control of a population dynamics model with hysteresis, 2022, 42, 0252-9602, 283, 10.1007/s10473-022-0116-x
    2. Kota Kumazaki, Toyohiko Aiki, Naoki Sato, Yusuke Murase, Multiscale model for moisture transport with adsorption phenomenon in concrete materials, 2018, 97, 0003-6811, 41, 10.1080/00036811.2017.1325473
    3. Toyohiko Aiki, Sergey A. Timoshin, Existence and uniqueness for a concrete carbonation process with hysteresis, 2017, 449, 0022247X, 1502, 10.1016/j.jmaa.2016.12.086
    4. Sergey A. Timoshin, Bang–Bang Control of a Prey–Predator Model with a Stable Food Stock and Hysteresis, 2023, 88, 0095-4616, 10.1007/s00245-023-09984-2
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2571) PDF downloads(107) Cited by(3)

Figures and Tables

Figures(7)  /  Tables(10)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog