电子科技大学学报2024，Vol.53Issue(1)：67-75,9.DOI:10.12178/1001-0548.2022307
基于兴趣注意力网络的会话推荐算法
Session-Based Recommender Algorithm Based on Interest Attention Network
摘要
Abstract
Aiming at the problems of insufficient extraction of users'main interest preferences in session-based recommender algorithms based on graph neural networks,a Session-Based Recommender Method Based on Interest Attention Network(SR-IAN)is proposed.First,the graph neural network is used to obtain the context transformation relationships between the items,and the graph embedding vectors of the items are obtained;Secondly,the graph embedding vector input into the interest attention network to extract the user's main interest preferences;Then the graph embedding vectors of the items are weighted by the attention layer;Finally,the click probability values of the candidate items are obtained through the prediction layer and sorted.The proposed algorithm model was verified by experiments on three public datasets Diginetica,Retailrocket and Tmall,which showed an improvement of 0.942%,1.183% and 2.977% compared with the baseline model on MRR@20.Besides,the time complexity of the model is reduced,which verifies the effectiveness and high efficiency of the proposed method.关键词
注意力机制/图神经网络/推荐算法/自注意力网络/会话推荐Key words
attention mechanism/graph neural network/recommender algorithm/self-attention network/session-based recommendation分类
信息技术与安全科学引用本文复制引用
崔少国,独潇,张宜浩..基于兴趣注意力网络的会话推荐算法[J].电子科技大学学报,2024,53(1):67-75,9.基金项目
重庆市自然科学基金面上项目(CSTB2022NSCQ-MSX1206) （CSTB2022NSCQ-MSX1206）
重庆市教委重点项目(KJZD-K202200510) （KJZD-K202200510）
重庆市科技局技术预见与制度创新项目(CSTB2022TFII-OFX0042) （CSTB2022TFII-OFX0042）
