Session-based recommendation algorithms only statically model a single preference of users and fail to capture the preference fluctuation of the users affected by the environment,thus reducing the recommendation accuracy.Therefore,this study proposes a session recommendation method that integrates dual-branch dynamic preferences.First,the heterogeneous hypergraph is used to model different types of information,and a dual-branch aggregation mechanism is designed to acquire and integrate the information in the heterogeneous hypergraph and learn the relationship between multiple types of nodes.Then,a price-embedded enhancer is used to strengthen the relationship between categories and prices.Second,a two-layer preference encoder is designed,which uses a multi-scale temporal Transformer to extract the user's dynamic price preference,and a soft attention mechanism and reverse position encoding are used to learn the user's dynamic interest preference.Finally,a gating mechanism is used to integrate the user's multi-type dynamic preferences and make recommendations to users.By conducting experiments on two datasets,namely Cosmetics and Diginetica-buy,the results prove that there is a significant improvement in Precision and MRR evaluation metrics compared with other algorithms.
万方数据
维普
