Knowledge Commons of Institute of Automation,CAS
Explainable Interaction-driven User Modeling over Knowledge Graph for Sequential Recommendatio | |
Huang, Xiaowen1,2![]() ![]() ![]() ![]() | |
2019-10 | |
会议名称 | ACM Multimedia 2019 |
页码 | 548-556 |
会议日期 | 2019.10.21-2019.10.27 |
会议地点 | Nice, France |
出版地 | 美国 |
出版者 | ACM |
摘要 | Compared with the traditional recommendation system, sequential recommendation holds the ability of capturing the evolution of users’ dynamic interests. Many previous studies in sequential recommendation focus on the accuracy of predicting the next item that a user might interact with, while generally ignore providing explanations why the item is recommended to the user. Appropriate explanations are critical to help users adopt the recommended item, and thus improve the transparency and trustworthiness of the recommendation system. In this paper, we propose a novel Explainable Interaction-driven User Modeling (EIUM) algorithm to exploit Knowledge Graph (KG) for constructing an effective and explainable sequential recommender. Qualified semantic paths between specific user-item pair are extracted from KG. Encoding those semantic paths and learning the importance scores for each path provides the path-wise explanation for the recommendation system. Different from traditional item-level sequential modeling methods, we capture the interaction-level user dynamic preferences by modeling the sequential interactions. It is a high-level representation which contains auxiliary semantic information from KG. Furthermore, we adopt a joint learning manner for better representation learning by employing multi-modal fusion, which benefits from the structural constraints in KG and involves three kinds of modalities. Extensive experiments on the large-scale dataset show the better performance of our approach in making sequential recommendations in terms of both accuracy and explainability. |
学科门类 | 工学::计算机科学与技术(可授工学、理学学位) |
DOI | 10.1145/3343031.3350893 |
URL | 查看原文 |
收录类别 | EI |
七大方向——子方向分类 | 推荐系统 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39189 |
专题 | 多模态人工智能系统全国重点实验室_多媒体计算 |
通讯作者 | Li, Yan |
作者单位 | 1.National Lab of Pattern Recognition, Institute of Automation, CAS,, Chinese Academy of Sciences 2.School of Artifcial Intelligence, University of Chinese Academy of Sciences 3.School of Computer and Information Technology & Beijing Key Lab of Trafc Data Analysis and Mining, Beijing Jiaotong University 4.Kuaishou Technology, Beijing 5.Peng Cheng Laboratory |
推荐引用方式 GB/T 7714 | Huang, Xiaowen,Fang, Quan,Qian, Shengsheng,et al. Explainable Interaction-driven User Modeling over Knowledge Graph for Sequential Recommendatio[C]. 美国:ACM,2019:548-556. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
3343031.3350893.pdf(2452KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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