Deep Reinforcement Learning-Based Recommendation Method with Positive and Negative Feedback State Representation
The application of deep reinforcement learning techniques in interactive recommendation systems has reached a high level of matu-rity.However,there is currently limited research dedicated to modeling there presentation of states.Existing works primarily focus on modeling state representations based on positive feedback sequences during user interactions.This approach results in the oversight of potential relation-ships existing within negative feedback sequences generated by users during interactions,as well as changes in user interests.Consequently,the recommendations produced by such systems tend to be one-sided.To address this gap,a novel recommendation system framework,named Contrastive Learning and Deep Reinforcement Learning-Based Recommender System(CRLRS),is proposed.CRLRS is designed to model state representations for both positive and negative feedback sequences generated during user interactions.Additionally,in order to mitigate data sparsity issues associated with positive feedback and address differences between fine-grained positive and negative feedback,a contras-tive auxiliary task is incorporated.Extensive experiments were conducted on two real-world datasets,among which HR@10 The results of the evaluation indicators on the Movielens-100k and Movielens-1m datasets are 0.705 2 and 0.490 2,respectively;NDCG@10 The results of the evaluation indicators are 0.478 2 and 0.271 5.The comparison results show that our method is significantly better than the current state-of-the-art methods,which proves the necessity of CRLRS modeling positive and negative feedback simultaneously and adding comparative auxil-iary tasks,and has better recommendation performance.
deep reinforcement learningcontrastive learningrecommender systempositive negative feedbackstate representation
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