Session-based Recommendation Algorithm Based on Memory Augmented Network
Session-based recommendation systems serve as essential tools for assisting users in identifying matching interests and requirements from large volumes of data.These systems aim to predict the next user actions based on anonymous sessions.However,existing methods inadequately represent the overall interests of a user and frequently neglect the positional relationships among items.To address this limitation,an enhanced memory network-based session recommendation model,SR-MAN,is proposed to analyze global user interest representations and item sequence problems.Initially,the method introduces position encoding during the generation of item embedding vectors to emphasize the impact of different positions on the sequence.Subsequently,a neural Turing machine is employed to store recent session information,and an attention network is designed to learn long-term user preferences by integrating the most recent user interaction as the current interest indicator.Finally,the method integrates long-term and current preferences to predict and recommend items of interest.Bayesian Personalized Ranking(BPR)is employed to estimate the model parameters.Experiments on three datasets demonstrate the effectiveness of the proposed method.
万方数据
维普
