ReInCre

enhancing collaborative filtering recommendations by incorporating user rating credibility

Naime Ranjbar Kermany*, Weiliang Zhao, Jian Yang, Jia Wu

*Corresponding author for this work

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

Abstract

We present ReInCre (Demo video available at https://youtu.be/MyFczz7Vefo) as a solution demo for incorporating user rating credibility in Collaborative Filtering (CF) approach to enhance the recommendation performance. The credibility values of users are calculated according to their rating behavior and they are utilized in discovering the neighbors (Code available at https://github.com/NaimeRanjbarKermany/Cred). To the best of our knowledge, it is the first work to incorporate the rating credibility of users in a CF recommendation. Our approach works as a powerful add-on to existing CF-based recommender systems in order to optimize the neighborhood. Experiments are conducted on the real-world dataset from Yahoo! Movies. Comparing with the baselines, the experimental results show that our proposed method significantly improves the quality of recommendation in terms of precision and F1-measure. In particular, the standard deviation of the errors between the prediction values and the real ratings becomes much smaller by incorporating credibility measurements of the users.

Original languageEnglish
Title of host publicationWeb Information Systems Engineering
Subtitle of host publicationWISE 2019 Workshop, Demo, and Tutorial, Revised Selected Papers
EditorsLeong Hou U, Jian Yang, Yi Cai, Kamalakar Karlapalem, An Liu, Xin Huang
Place of PublicationSingapore
PublisherSpringer, Springer Nature
Pages64-72
Number of pages9
ISBN (Electronic)9789811532818
ISBN (Print)9789811532801
DOIs
Publication statusPublished - 2020
Event20th International Conference on Web Information Systems Engineering, WISE 2019 and on the International Workshop on Web Information Systems in the Era of AI, 2019 - Hong Kong, China
Duration: 19 Jan 202022 Jan 2020

Publication series

NameCommunications in Computer and Information Science
Volume1155 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference20th International Conference on Web Information Systems Engineering, WISE 2019 and on the International Workshop on Web Information Systems in the Era of AI, 2019
CountryChina
CityHong Kong
Period19/01/2022/01/20

Keywords

  • Collaborative filtering
  • Neighbor optimization
  • User credibility
  • User rating behavior

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  • Cite this

    Kermany, N. R., Zhao, W., Yang, J., & Wu, J. (2020). ReInCre: enhancing collaborative filtering recommendations by incorporating user rating credibility. In L. H. U, J. Yang, Y. Cai, K. Karlapalem, A. Liu, & X. Huang (Eds.), Web Information Systems Engineering: WISE 2019 Workshop, Demo, and Tutorial, Revised Selected Papers (pp. 64-72). (Communications in Computer and Information Science; Vol. 1155 CCIS). Singapore: Springer, Springer Nature. https://doi.org/10.1007/978-981-15-3281-8_7