Aiming to address the limitations of current session-based recommendation methods,which primarily only focus on the context of a single interest and neglect the multiple interests of a single user,and overlook the utilization of multimodal data types when incorporating knowledge graphs,a cross-session recommendation algorithm based on multi-interest point fusion and multi-modal knowledge graph was proposed.Inspired by the successful application of few-shot learning in limited instance lear-ning models,a cross-session collaboration network was designed and the next item recommendation was modeled as a few-shot learning problem.Various interests of users were captured from their behavior sequences and an interest graph based on their historical and current behavior sequences was constructed.The modal knowledge graph attention network was introduced,by using the multi-modal graph attention mechanism,the information was disseminated,aggregated embedding representation was obtained and recommendations were made.A similar session retrieval network was designed to find networks similar to the cur-rent session from historical sessions to supplement and optimize the preference representation.Experimental results indicate that the proposed algorithm exhibits excellent performance in terms of Recall@20 and MRR@20 compared to the baseline.
关键词
会话推荐/兴趣点/知识图谱/少样本学习/多模态/图注意力/用户偏好
Key words
session recommendation/multiple points of interest/knowledge graph/small sample learning/multimodal/image attention/user preferences
维普
万方数据