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K-truss community most favorites query based on top-t

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Abstract

Top-t queries, which return t results with the highest scores according to the user’s preferences, have been extensively studied. However, many studies often fail to take into account the social relationships among users, and close social relationships usually guarantee the consistency of users’ preferences. Many real-world applications, such as community-based product recommendation services, require such queries. In this paper, we propose a new problem: the k-truss community most favorites querying problem based on user top-t favorites. Specifically, both the k-truss community and the corresponding most favorite object are retrieved. We first present the baseline solution to the problem. The main idea is to find the same object in the top-t favorites of the users in the social network and build the k-truss community for these users with the same preferences. Furthermore, we propose a reverse query algorithm to speed up the processing of the k − t CMF querying problem by filtering users in the social network. The experiment results on both real and synthetic datasets significantly demonstrate that the proposed solutions are efficient and scalable.

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  1. http://www.yelp.com

  2. snap.stanford.edu

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Acknowledgements

The research was supported by the National Key Research and Development Program of China (2018YFB0204302) and the NSFC (Grant Nos. 61772182, 61802032).

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Authors and Affiliations

  1. College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, Hunan, 410003, China

    Zhibang Yang

  2. College of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China

    Zhibang Yang, Xiaoxue Li, Xu Zhang, Wensheng Luo & Kenli Li

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  1. Zhibang Yang
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  2. Xiaoxue Li
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Correspondence to Xiaoxue Li.

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This article belongs to the Topical Collection: Special Issue on Large Scale Graph Data Analytics Guest Editors: Xuemin Lin, Lu Qin, Wenjie Zhang, and Ying Zhang

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Yang, Z., Li, X., Zhang, X. et al. K-truss community most favorites query based on top-t. World Wide Web 25, 949–969 (2022). https://doi.org/10.1007/s11280-021-00947-7

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  • DOI: https://doi.org/10.1007/s11280-021-00947-7

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