? Illuminating Recommendation by Understanding the Explicit Item Relations
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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 Current Issue | Archive | Adv Search << Previous Articles | Next Articles >>
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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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.
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Keywordsrecommender system   item relation   recommendation interpretability     
Received 2018-01-15;

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.

Corresponding Authors: 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.
Cite this article:   
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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