An incremental scheme for large-scale social-based recommender systems

Chia Ling Hsiao, Zih Syuan Wang, Wei Guang Teng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Nowadays, recommender systems have become a necessity in various applications, especially in a large-scale online shop. In addition to the rating information provided by the users, social relationships of a user begin to be incorporated to further improve the performance of current recommender systems. Among several alternatives, matrix factorization is recognized as an effective technique to reduce data dimensionality and to capture significant latent relationships between users and items. Furthermore, recommender systems are used in an ever-changing commercial environment and usually operate on the large-scale data. Note that there are always new users, items and ratings as time advances, resulting in a rating matrix of increasing size. This poses a challenging problem because decomposing the entire matrix is costly. In this work, we thus propose an incremental scheme to directly update the rating matrix without the need to decompose the entire rating matrix. This helps to achieve better efficiency at the cost of some approximation errors. Experimental results show that our scheme has high efficiency as expected and significantly enhances the recommendation quality for cold-start users.

Original languageEnglish
Title of host publicationDSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics
EditorsGeorge Karypis, Longbing Cao, Wei Wang, Irwin King
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages128-134
Number of pages7
ISBN (Electronic)9781479969913
DOIs
Publication statusPublished - 2014 Mar 10
Event2014 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2014 - Shanghai, China
Duration: 2014 Oct 302014 Nov 1

Publication series

NameDSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics

Other

Other2014 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2014
CountryChina
CityShanghai
Period14-10-3014-11-01

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems
  • Information Systems and Management

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  • Cite this

    Hsiao, C. L., Wang, Z. S., & Teng, W. G. (2014). An incremental scheme for large-scale social-based recommender systems. In G. Karypis, L. Cao, W. Wang, & I. King (Eds.), DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics (pp. 128-134). [7058063] (DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSAA.2014.7058063