面向重复消费场景的会话推荐算法研究  

Session-Based Recommendation Algorithm for Repeat Consumption Scenarios

作　　者：田甜俊子 朱学芳[1] Tian Tianjunzi;Zhu Xuefang(School of Information Management,Nanjing University,Nanjing 210023,China)

机构地区：[1]南京大学信息管理学院,南京210023

出　　处：《数据分析与知识发现》2023年第4期89-100,共12页Data Analysis and Knowledge Discovery

摘　　要：【目的】提升会话推荐模型在重复消费场景中的性能,减轻信息过载带来的负面影响。【方法】对适用于重复消费场景的重复-探索机制进行改进,并基于自注意力机制,采取非侵入性方式融合位置信息,优化边信息利用效果。新模型性能在公共数据集上得到了验证。【结果】相较于次优值,新模型在Yoochoose 1/64数据集上的召回率、平均排序倒数分别提升0.71%、1.69%;在Diginetica数据集上的召回率、平均排序倒数分别提升3.08%、5.72%。【局限】实验仅使用位置信息作为边信息进行验证,且用于验证的数据集有限。【结论】实验结果验证了所提模型对重复-探索机制的改进、对非侵入性边信息的利用具备有效性,能为推荐系统提供新的优化思路,进而提高个性化信息服务水平。[Objective]This study aims to improve the performance of session-based recommendation models in repeat consumption scenarios and reduce the negative impact of information overload.[Methods]First,we improved the Repeat-Explore Mechanism suitable for repeat consumption scenarios.Then,based on SelfAttention Mechanism,we fused the position information in a non-invasive approach to optimize the utilization of side information.The performance of the new model was validated on public datasets.[Results]Compared to the suboptimal values,the Recall and Mean Reciprocal Rank of the new model on the Yoochoose 1/64 dataset increased by 0.71%and 1.69%,respectively.On the Diginetica dataset,the Recall and Mean Reciprocal Rank were improved by 3.08%and 5.72%.[Limitations]Our experiment only used position information as side information,and the datasets used for verification were limited.[Conclusions]The experimental results verify the effectiveness of the proposed model,which could optimize recommendation systems and improve personalized information services.

关 键 词：会话推荐 序列推荐 自注意力网络 重复消费 边信息 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]