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Interactive resource recommendation algorithm based on tag information

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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.

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Notes

  1. Some tag categories may fail to fully divide the entire resource set due to miss tagging, so we only consider those categories covering all resource items

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China

    Qing Xie, Feng Xiong, Tian Han, Yongjian Liu & Lin Li

  2. School of Computer Science and Information Technology, RMIT University, Melbourne, Australia

    Zhifeng Bao

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  1. Qing Xie
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  2. Feng Xiong
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  3. Tian Han
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  4. Yongjian Liu
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  5. Lin Li
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  6. Zhifeng Bao
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Corresponding author

Correspondence to Qing Xie.

Additional information

This article belongs to the Topical Collection: Special Issue on Deep Mining Big Social Data

Guest Editors: Xiaofeng Zhu, Gerard Sanroma, Jilian Zhang, and Brent C. Munsell

Part of the results in this work appeared in proceedings of the 13th International Conference on Semantics, Knowledge and Grids on Big Data [32].

This research is partially supported by Natural Science Foundation of China (Grant No.61602353), National Social Science Foundation of China (Grant No.15BGL048) and the Fundamental Research Funds for the Central Universities (WUT:2017IVA053, WUT:2017IVB028 and WUT:2017II39GX).

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Xie, Q., Xiong, F., Han, T. et al. Interactive resource recommendation algorithm based on tag information. World Wide Web 21, 1655–1673 (2018). https://doi.org/10.1007/s11280-018-0532-y

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  • DOI: https://doi.org/10.1007/s11280-018-0532-y

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