山西大学学报（自然科学版）2024，Vol.47Issue(3)：471-480,10.DOI:10.13451/j.sxu.ns.2024004
基于稳定学习的多兴趣序列推荐网络
Sequential Recommendation of Multi-Interest Network Based on Stable Learning
摘要
Abstract
Multi-interest models,which extract interests of a user as multiple representation vectors,have shown promising perfor-mances for sequential recommendation.However,considering that multiple interests of a user are usually highly correlated,the mod-el has chance to learn spurious correlations between noisy interests and target items.Once the data distribution changes,the correla-tions among interests may also change,and the spurious correlations will mislead the model to make wrong predictions.To solve such problem,we propose a novel model of Multi-Interest network with Stable Learning(MISL),which attempts to de-correlate the extracted interests,and thus spurious correlations can be eliminated.MISL applies an attentive module to extract multiple interests,and then selects the most important one for making final predictions.Meanwhile,MISL incorporates a weighted correlation estima-tion loss based on independence criterion,with which training samples are weighted,to minimize the correlations among extracted interests.Extensive experiments have been conducted under both Out-of-Distribution(OOD)and random settings,and up to 36.8%and 21.7%relative improvements are achieved respectively.关键词
神经网络/信息检索/推荐系统Key words
neural networks/information retrieval/recommendation system分类
信息技术与安全科学引用本文复制引用
刘昭呈,朱振熙,刘强..基于稳定学习的多兴趣序列推荐网络[J].山西大学学报（自然科学版）,2024,47(3):471-480,10.基金项目
国家自然科学基金(62206291) （62206291）
