Personalizing the settings for CF-based recommender systems

Il Im, Byung Ho Kim

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

3 Citations (Scopus)

Abstract

In this paper, we propose a new method for collaborative filtering (CF)-based recommender systems. Traditional CF-based recommendation algorithms have applied constant settings such as a reference group (neighborhood) size and a significance level to all users. In this paper we develop a new method that identifies optimal personalized settings for each user and applies them to generating recommendations for individual users. Personalized parameters are identified through iterative simulations with 'training' and 'verification' datasets. The method is compared with traditional 'constant settings' methods using Netflix data. The results show that the new method outperforms traditional, ordinary CF. Implications and future research directions are also discussed.

Original languageEnglish
Title of host publicationRecSys'10 - Proceedings of the 4th ACM Conference on Recommender Systems
Pages245-248
Number of pages4
DOIs
Publication statusPublished - 2010 Dec 15
Event4th ACM Recommender Systems Conference, RecSys 2010 - Barcelona, Spain
Duration: 2010 Sep 262010 Sep 30

Publication series

NameRecSys'10 - Proceedings of the 4th ACM Conference on Recommender Systems

Other

Other4th ACM Recommender Systems Conference, RecSys 2010
CountrySpain
CityBarcelona
Period10/9/2610/9/30

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems
  • Software
  • Control and Systems Engineering

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  • Cite this

    Im, I., & Kim, B. H. (2010). Personalizing the settings for CF-based recommender systems. In RecSys'10 - Proceedings of the 4th ACM Conference on Recommender Systems (pp. 245-248). (RecSys'10 - Proceedings of the 4th ACM Conference on Recommender Systems). https://doi.org/10.1145/1864708.1864758