TyCo: Towards typicality-based collaborative filtering recommendation

Yi Cai, Ho Fung Leung, Qing Li, Jie Tang, Juanzi Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)

Abstract

Collaborative filtering (CF) is an important and popular technology for recommendation systems. However, current collaborative filtering methods suffer from some problems such as sparsity problem, inaccurate recommendation and producing big-error predictions. In this paper, we borrow ideas of object typicality from cognitive psychology and propose a novel typicality-based collaborative filtering recommendation method named TyCo. A distinct feature of typicality-based CF is that it finds 'neighbors' of users based on user typicality degrees in user groups (instead of the co-rated items of users or common users of items in traditional CF). To the best of our knowledge, there is no work on investigating collaborative filtering recommendation by combining object typicality. We conduct experiments to validate TyCo and compare it with previous methods.

Original languageEnglish
Title of host publicationProceedings - 22nd International Conference on Tools with Artificial Intelligence, ICTAI 2010
Pages97-104
Number of pages8
DOIs
Publication statusPublished - 1 Dec 2010
Externally publishedYes
Event22nd International Conference on Tools with Artificial Intelligence, ICTAI 2010 - Arras, France
Duration: 27 Oct 201029 Oct 2010

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume2
ISSN (Print)1082-3409

Conference

Conference22nd International Conference on Tools with Artificial Intelligence, ICTAI 2010
CountryFrance
CityArras
Period27/10/1029/10/10

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Science Applications

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