端到端顺序推荐的多利益多样化

Wanyu Chen, Pengjie Ren, Fei Cai, Fei Sun, Maarten de Rijke
{"title":"端到端顺序推荐的多利益多样化","authors":"Wanyu Chen, Pengjie Ren, Fei Cai, Fei Sun, Maarten de Rijke","doi":"10.1145/3475768","DOIUrl":null,"url":null,"abstract":"Sequential recommenders capture dynamic aspects of users’ interests by modeling sequential behavior. Previous studies on sequential recommendations mostly aim to identify users’ main recent interests to optimize the recommendation accuracy; they often neglect the fact that users display multiple interests over extended periods of time, which could be used to improve the diversity of lists of recommended items. Existing work related to diversified recommendation typically assumes that users’ preferences are static and depend on post-processing the candidate list of recommended items. However, those conditions are not suitable when applied to sequential recommendations. We tackle sequential recommendation as a list generation process and propose a unified approach to take accuracy as well as diversity into consideration, called multi-interest, diversified, sequential recommendation. Particularly, an implicit interest mining module is first used to mine users’ multiple interests, which are reflected in users’ sequential behavior. Then an interest-aware, diversity promoting decoder is designed to produce recommendations that cover those interests. For training, we introduce an interest-aware, diversity promoting loss function that can supervise the model to learn to recommend accurate as well as diversified items. We conduct comprehensive experiments on four public datasets and the results show that our proposal outperforms state-of-the-art methods regarding diversity while producing comparable or better accuracy for sequential recommendation.","PeriodicalId":6934,"journal":{"name":"ACM Transactions on Information Systems (TOIS)","volume":"56 1","pages":"1 - 30"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"Multi-interest Diversification for End-to-end Sequential Recommendation\",\"authors\":\"Wanyu Chen, Pengjie Ren, Fei Cai, Fei Sun, Maarten de Rijke\",\"doi\":\"10.1145/3475768\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Sequential recommenders capture dynamic aspects of users’ interests by modeling sequential behavior. Previous studies on sequential recommendations mostly aim to identify users’ main recent interests to optimize the recommendation accuracy; they often neglect the fact that users display multiple interests over extended periods of time, which could be used to improve the diversity of lists of recommended items. Existing work related to diversified recommendation typically assumes that users’ preferences are static and depend on post-processing the candidate list of recommended items. However, those conditions are not suitable when applied to sequential recommendations. We tackle sequential recommendation as a list generation process and propose a unified approach to take accuracy as well as diversity into consideration, called multi-interest, diversified, sequential recommendation. Particularly, an implicit interest mining module is first used to mine users’ multiple interests, which are reflected in users’ sequential behavior. Then an interest-aware, diversity promoting decoder is designed to produce recommendations that cover those interests. For training, we introduce an interest-aware, diversity promoting loss function that can supervise the model to learn to recommend accurate as well as diversified items. We conduct comprehensive experiments on four public datasets and the results show that our proposal outperforms state-of-the-art methods regarding diversity while producing comparable or better accuracy for sequential recommendation.\",\"PeriodicalId\":6934,\"journal\":{\"name\":\"ACM Transactions on Information Systems (TOIS)\",\"volume\":\"56 1\",\"pages\":\"1 - 30\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Information Systems (TOIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3475768\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Information Systems (TOIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3475768","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21

摘要

顺序推荐通过对顺序行为建模来捕获用户兴趣的动态方面。以往关于顺序推荐的研究主要是为了识别用户最近的主要兴趣,以优化推荐的准确性;他们经常忽略这样一个事实,即用户会在很长一段时间内显示多种兴趣,这可以用来改善推荐项目列表的多样性。与多样化推荐相关的现有工作通常假设用户的偏好是静态的,并且依赖于对推荐项目候选列表的后处理。但是,这些条件不适用于顺序建议。我们将顺序推荐作为一个列表生成过程来处理,并提出了一种兼顾准确性和多样性的统一方法,称为多兴趣、多样化、顺序推荐。其中,首先使用隐式兴趣挖掘模块挖掘用户的多个兴趣,这些兴趣反映在用户的顺序行为中。然后设计一个兴趣感知,多样性促进解码器,以产生涵盖这些兴趣的建议。对于训练,我们引入了一个兴趣感知、多样性促进损失函数,可以监督模型学习推荐准确和多样化的项目。我们在四个公共数据集上进行了全面的实验,结果表明,我们的建议在多样性方面优于最先进的方法,同时对顺序推荐产生相当或更好的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-interest Diversification for End-to-end Sequential Recommendation
Sequential recommenders capture dynamic aspects of users’ interests by modeling sequential behavior. Previous studies on sequential recommendations mostly aim to identify users’ main recent interests to optimize the recommendation accuracy; they often neglect the fact that users display multiple interests over extended periods of time, which could be used to improve the diversity of lists of recommended items. Existing work related to diversified recommendation typically assumes that users’ preferences are static and depend on post-processing the candidate list of recommended items. However, those conditions are not suitable when applied to sequential recommendations. We tackle sequential recommendation as a list generation process and propose a unified approach to take accuracy as well as diversity into consideration, called multi-interest, diversified, sequential recommendation. Particularly, an implicit interest mining module is first used to mine users’ multiple interests, which are reflected in users’ sequential behavior. Then an interest-aware, diversity promoting decoder is designed to produce recommendations that cover those interests. For training, we introduce an interest-aware, diversity promoting loss function that can supervise the model to learn to recommend accurate as well as diversified items. We conduct comprehensive experiments on four public datasets and the results show that our proposal outperforms state-of-the-art methods regarding diversity while producing comparable or better accuracy for sequential recommendation.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信