Interactive resource recommendation algorithm based on tag information

Xiong, F, Han, T, Liu, Y, Li, L and Bao, Z 2018, 'Interactive resource recommendation algorithm based on tag information', World Wide Web, vol. 21, no. 6, pp. 1655-1673.


Document type: Journal Article
Collection: Journal Articles

Title Interactive resource recommendation algorithm based on tag information
Author(s) Xiong, F
Han, T
Liu, Y
Li, L
Bao, Z
Year 2018
Journal name World Wide Web
Volume number 21
Issue number 6
Start page 1655
End page 1673
Total pages 19
Publisher Springer New York LLC
Abstract With the popularization of social media and the exponential growth of information generated by online users, the recommender system has been popular in helping users to find the desired resources from vast amounts of data. However, the cold-start problem is one of the major challenges for personalized recommendation. In this work, we utilized the tag information associated with different resources, and proposed a tag-based interactive framework to make the resource recommendation for different users. During the interaction, the most effective tag information will be selected for users to choose, and the approach considers the users' feedback to dynamically adjusts the recommended candidates during the recommendation process. Furthermore, to effectively explore the user preference and resource characteristics, we analyzed the tag information of different resources to represent the user and resource features, considering the users' personal operations and time factor, based on which we can identify the similar users and resource items. Probabilistic matrix factorization is employed in our work to overcome the rating sparsity, which is enhanced by embedding the similar user and resource information. The experiments on real-world datasets demonstrate that the proposed algorithm can get more accurate predictions and higher recommendation efficiency.
Subject Database Management
Global Information Systems
Keyword(s) Collaborative filtering
Interactive recommendation
Probabilistic matrix factorization
Tag information
DOI - identifier 10.1007/s11280-018-0532-y
Copyright notice © Springer Science+Business Media, LLC, part of Springer Nature 2018
ISSN 1386-145X
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