关键词: 推荐算法, 上下文, 会话, 循环神经网络

Abstract:

In view of the current situation that the current session recommendation algorithm does not fully consider the user’s context information, in order to enhance the personalized recommendation effect of the session-based recommendation algorithm, a context-aware recommendation algorithm fused with user session data is proposed. Context information is mapped to low-dimensional real vector features through embedding, and low-dimensional vector features are incorporated into a session-based recurrent neural network recommendation model through three combinations of Add, Stack, and MLP. A loss function based on BPR is designed to dynamically depict the user preference of session sequence, which enhances personalized recommendation capabilities. Experiments on the Adressa dataset show that compared with the baseline algorithm GRU4REC, the proposed algorithm improves the indicator Recall@20 by 3.2% and MRR@20 by 27%.

Key words: recommendation algorithm, context, session, recurrent neural network