DRCGR: Deep Reinforcement Learning Framework Incorporating CNN and GANBased for Interactive Recommendation Recently, the application of deep reinforcement learning into the field of sessionbased interactive recommendation has attracted great attention from researchers. However, despite that some interactive recommendation models based on deep reinforcement learning have been proposed, they still suffer to the following limitations: (1) these works ignore the skip behaviors of sequential patterns in usersu0027 clicking behavior; (2) these works fail to incorporate positive feedback and negative feedback into the proposed deep reinforcement recommender system when the positive feedback is sparse. Therefore, to solve the problems mentioned above, a novel Deep QNetwork based recommendation framework incorporating CNN and GANbased models is proposed to acquire robust performance, named DRCGR. Specifically, in DRCGR, a CNN model is used to capture the sequential features for positive feedback. Then, an adversarial training is adopted to learn optimal negative feedback representations Then, positivenegative representations are fed into DQN simultaneously, which are conducive to generating better actionvalue function The experimental results based on realworld ecommerce data demonstrate our frameworku0027s superiority over some stateoftheart recommendation models.

Joint Neural Collaborative Filtering for Recommender Systems. We propose a JNCF method for recommender systems. The JNCF model applies a joint neural network that couples deep feature learning and deep interaction modeling with a rating matrix. Deep feature learning extracts feature representations of users and items with a deep learning architecture based on a useritem rating matrix. Deep interaction modeling captures nonlinear useritem interactions with a deep neural network using the feature representations generated by the deep feature learning process as input. JNCF enables the deep feature learning and deep interaction modeling processes to optimize each other through joint training, which leads to improved recommendation performance. In addition, we design a new loss function for optimization, which takes both implicit and explicit feedback, pointwise and pairwise loss into account. Experiments on several realword datasets show significant improvements of JNCF over stateoftheart methods, with improvements of up to 8.24% on the MovieLens 100K dataset, 10.81% on the MovieLens 1M dataset, and 10.21% on the Amazon Movies dataset in terms of HR10. NDCG10 improvements are 12.42%, 14.24% and 15.06%, respectively. We also conduct experiments to evaluate the scalability and sensitivity of JNCF. Our experiments show that the JNCF model has a competitive recommendation performance with inactive users and different degrees of data sparsity when compared to stateoftheart baselines.