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Journal of Computer Science and Technology

, Volume 33, Issue 4, pp 739–755 | Cite as

Illuminating Recommendation by Understanding the Explicit Item Relations

  • Qi Liu
  • Hong-Ke Zhao
  • Le Wu
  • Zhi Li
  • En-Hong Chen
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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.

Keywords

recommender system item relation recommendation interpretability 

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Qi Liu
    • 1
  • Hong-Ke Zhao
    • 1
  • Le Wu
    • 2
  • Zhi Li
    • 1
    • 3
  • En-Hong Chen
    • 1
  1. 1.Anhui Province Key Laboratory of Big Data Analysis and ApplicationUniversity of Science and Technology of ChinaHefeiChina
  2. 2.School of Computer and InformationHefei University of TechnologyHefeiChina
  3. 3.School of Software EngineeringUniversity of Science and Technology of ChinaHefeiChina

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