? 通过物品的显式关系理解推荐系统
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Journal of Computer Science and Technology 2018, Vol. 33 Issue (4) :739-755    DOI: 10.1007/s11390-018-1853-0
Special Issue on Software Engineering for High-Confidence Systems << Previous Articles | Next Articles >>
通过物品的显式关系理解推荐系统
Qi Liu1, Member, ACM, IEEE, Hong-Ke Zhao1, Le Wu2, Zhi Li1,3, En-Hong Chen1,*, Fellow, CCF, Senior Member, IEEE
1 Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China Hefei 230027, China;
2 School of Computer and Information, Hefei University of Technology, Hefei 230009, China;
3 School of Software Engineering, University of Science and Technology of China, Hefei 230051, China
Illuminating Recommendation by Understanding the Explicit Item Relations
Qi Liu1, Member, ACM, IEEE, Hong-Ke Zhao1, Le Wu2, Zhi Li1,3, En-Hong Chen1,*, Fellow, CCF, Senior Member, IEEE
1 Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China Hefei 230027, China;
2 School of Computer and Information, Hefei University of Technology, Hefei 230009, China;
3 School of Software Engineering, University of Science and Technology of China, Hefei 230051, China

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摘要 近些年见证了推荐系统在众多应用领域的普及。推荐系统基于多源信息为每个用户生成一个可供选择的推荐物品列表。在很长一段时间里,大多数的研究者追求推荐系统在特定指标上的表现效果,例如,准确性。然而,在现实社会中,用户从大量的商品中选择物品时会主要考虑他们内部的需求和外部的约束。因此,我们认为,在具体的应用领域中显式地建模物品关系对于理解推荐系统而言十分有必要。事实上,在该领域,研究者已经做了一些相关工作,对推荐过程的理解也逐步从隐式转向显式的角度。因此,在这篇文章中,我们从物品显式关系理解的角度整理了推荐系统领域最近的研究进展。我们主要从三个方面来组织相关工作,即:物品组合效应关系,序列依赖关系和外部约束关系。具体来说,组合效应关系和序列依赖关系的相关工作从用户的需求角度建模物品的内部关系,而外部约束关系则强调物品之间的外部要求关系。在此之后,我们也提出了在物品显性关系方面的开放性问题和在推荐系统领域未来的研究建议。
关键词推荐系统   物品关系   推荐解释性     
Abstract: Recent years have witnessed the prevalence of recommender systems in various fields, which provide a personalized recommendation list for each user based on various kinds of information. For quite a long time, most researchers have been pursing recommendation performances with predefined metrics, e.g., accuracy. However, in real-world applications, users select items from a huge item list by considering their internal personalized demand and external constraints. Thus, we argue that explicitly modeling the complex relations among items under domain-specific applications is an indispensable part for enhancing the recommendations. Actually, in this area, researchers have done some work to understand the item relations gradually from "implicit" to "explicit" views when recommending. To this end, in this paper, we conduct a survey of these recent advances on recommender systems from the perspective of the explicit item relation understanding. We organize these relevant studies from three types of item relations, i.e., combination-effect relations, sequence-dependence relations, and external-constraint relations. Specifically, the combination-effect relation and the sequence-dependence relation based work models the intra-group intrinsic relations of items from the user demand perspective, and the external-constraint relation emphasizes the external requirements for items. After that, we also propose our opinions on the open issues along the line of understanding item relations and suggest some future research directions in recommendation area.
Keywordsrecommender system   item relation   recommendation interpretability     
Received 2018-01-15;
本文基金:

This research was partially supported by the National Natural Science Foundation of China under Grant Nos. U1605251, 61672483 and 61602147, and the Fundamental Research Funds for the Central Universities of China under Grant No. JZ2016HGBZ0749. Qi Liu gratefully acknowledges the support of the Young Elite Scientist Sponsorship Program of China Association for Science and Technology (CAST) and the Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) under Grant No. 2014299.

通讯作者: En-Hong Chen,E-mail:cheneh@ustc.edu.cn     Email: cheneh@ustc.edu.cn
About author: Qi Liu received his Ph.D. degree in computer science from University of Science and Technology of China (USTC), Hefei, in 2013. He is an associate professor with USTC, Hefei. His general area of research is data mining and knowledge discovery. He has published prolifically in refereed journals and conference proceedings, e.g., IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Information Systems, ACM Transactions on Knowledge Discovery from Data, ACM Transactions on Intelligent Systems and Technology, ACM SIGKDD, IJCAI, AAAI, IEEE ICDM, SDM, and ACM CIKM. He has served regularly on the program committees of a number of conferences, and is a reviewer for the leading academic journals in his fields. He received the ICDM 2011 Best Research Paper Award. He is a member of ACM and IEEE.
引用本文:   
Qi Liu, Hong-Ke Zhao, Le Wu, Zhi Li, En-Hong Chen.通过物品的显式关系理解推荐系统[J]  Journal of Computer Science and Technology , 2018,V33(4): 739-755
Qi Liu, Hong-Ke Zhao, Le Wu, Zhi Li, En-Hong Chen.Illuminating Recommendation by Understanding the Explicit Item Relations[J]  Journal of Computer Science and Technology, 2018,V33(4): 739-755
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