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Illuminating Recommendation by Understanding the Explicit Item Relations

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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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Authors and Affiliations

  1. Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China, Hefei, 230027, China

    Qi Liu, Hong-Ke Zhao, Zhi Li & En-Hong Chen

  2. School of Computer and Information, Hefei University of Technology, Hefei, 230009, China

    Le Wu

  3. School of Software Engineering, University of Science and Technology of China, Hefei, 230051, China

    Zhi Li

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Liu, Q., Zhao, HK., Wu, L. et al. Illuminating Recommendation by Understanding the Explicit Item Relations. J. Comput. Sci. Technol. 33, 739–755 (2018). https://doi.org/10.1007/s11390-018-1853-0

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