RSEIU: A recommendation algorithm for shopping based on explicit and implicit feedback of user

Business websites typically employ recommendation systems to process large amounts of information. The lack of behavioral data for new users leads to the system’s inability to make recommendations for them, which is known as the cold-start problem. Additionally, these algorithms heavily rely on sparse explicit feedback data, making it challenging to provide users with precise recommendations , leading to the data sparsity problem. To address these issues, a recommendation algorithm for shopping based on explicit and implicit feedback from the user is proposed. Firstly, the algorithm uses special filters to extract features and generate a candidate list. Secondly, the interaction learning model and the multi-implicit feedback learning model are used to extract implicit relationships from the rating data for each item in the candidate list. The multi-implicit feedback learning model analyzes the implicit relationship from three perspectives. The three types of implicit feedback are used as auxiliary data to solve the data sparsity problem. The effectiveness of the algorithm in shopping recommendation is validated using the Tafeng dataset and the BookCrossing dataset.