基于全局一致性增强的多偏好会话推荐模型  

Global Consistency Augmented Multi-preference Session-Based Recommendation Model

作　　者：吴江铭 张晓堃 徐博[1] 杨亮[1] 林鸿飞[1] WU Jiangming;ZHANG Xiaokun;XU Bo;YANG Liang;LIN Hongfei(School of Computer Science and Technology,Dalian University of Technology,Dalian 116024)

机构地区：[1]大连理工大学计算机科学与技术学院,大连116024

出　　处：《模式识别与人工智能》2024年第6期513-524,共12页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.62076046,62006034)资助。

摘　　要：基于会话的推荐旨在根据一组匿名会话预测用户下一个可能交互的物品.现有的基于图神经网络的会话推荐模型对全局信息的利用不足.为此,文中提出基于全局一致性增强的多偏好会话推荐模型(Global Consistency Augmented Multi-preference Session-Based Recommendation Model,GCAM).首先,在利用全局信息时,通过最短路径搜索算法构建一致性全局图,捕捉强依赖的物品关系,过滤不可靠的物品关系,从而保证全局信息的一致性.然后,应用一种多偏好标签平滑策略,从历史会话中充分挖掘协同信息,对标签进行平滑化,拟合用户偏好的真实分布.在3个数据集上的大量实验表明GCAM的优越性.Session-based recommendation aims to predict the next item which a user is likely to interact with based on an anonymous session.However,existing session-based recommendation methods based on graph neural networks underutilize the global information.To address this issue,a global consistency augmented multi-preference session-based recommendation model(GCAM)is proposed.Firstly,a consistent global graph is constructed through the shortest path routing algorithm.The consistency of global information is ensured by capturing reliable item relationships and filtering out unreliable item relationships.Secondly,a multi-preference label smoothing strategy is applied to mine collaborative information from historical sessions to soften labels,and thereby the label can fit the true user preferences.Extensive experiments on three different datasets demonstrate the superiority of GCAM.

关 键 词：会话推荐 多偏好学习 自监督学习 全局一致性增强 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]