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In recent years, with the development of Internet technology, recommender systems have been widely used by virtue of their ability to meet the personalized needs of users. In order to make full use of users' interactive behaviors, session-based recommender systems have attracted growing research interest. In previous session-based recommender systems, users' historical interactive behavior is utilized to train and update users' preferences, but users' responses to the current recommendation results (immediate feedback) are not effectively exploited to optimize the recommendation strategy. This leads to the decrease of subsequent recommendation accuracy. Aiming at this problem, based on the Recurrent Neural Network (RNN), this paper combines Reinforcement Learning (RL) and Generative Adversarial Networks (GANs). We fully exploit the users' immediate feedback with RL, and simultaneously take advantage of GAN to satisfy the requirement of training data brought by RL. Furthermore, we optimize the negative sampling method and propose Deep Generative Adversarial Networks-based Collaborative Filtering (DCFGAN). The experimental results show that this algorithm can effectively improve the recommendation accuracy in session-based recommender systems. [ABSTRACT FROM AUTHOR]

Recommender systems offer an efficient solution to the problem of information overload, which is exacerbated by the rapid expansion of data. Context-aware recommender systems (CARS) have become prominent, incorporating contextual information to provide more desirable recommendations. While the dimension of contextual information rises, the complexity of algorithms increases as well, highlighting the data sparsity problem. In this paper, we propose CTITF, a novel tensor factorization model that incorporates constrained user bidirectional trust and implicit feedback. First, CTITF distinguishes the roles of trust networks in terms of the directionality of trust relationships: the trustor and the trustee. CTITF then constructs trust influences and biases for both roles to linearly represent user preferences. Furthermore, CTITF improves the rating prediction function and recommendation objective function based on CP factorization. Finally, CTITF filters out trust relationships where users do not have the same history items. It also uses contextual information to limit the amount of implicit feedback, addressing the issue of high time complexity. Experiments on two real-world datasets illustrate that the proposed model outperforms previous TF-based models and social recommender systems in terms of rating prediction accuracy. In addition, the CTITF model reduces the training time by 50.28% compared to the trustTF model, which also uses trust. [ABSTRACT FROM AUTHOR]

This paper investigates the design problem of the sampled-data state feedback control (SDSFC) for evolutionary threshold public goods games (PGGs) with two enhancement factors on coupled networks. Firstly, using the semi-tensor product (STP) of matrices, an algebraic form for the dynamics of the controlled coupled PGGs is established. Secondly, based on the algebraic expression, the conditions to make the accumulated contribution of all players in each game reach its own threshold under SDSFCs are provided, and the SDSFCs are derived. Finally, an illustrative example is given to show the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]