Context-aware item-to-item recommendation within the factorization framework

Balázs Hidasi, Domonkos Tikk

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

7 Citations (Scopus)

Abstract

Item-to-item recommendation - when the most similar items sought to the actual item - is an important recommendation scenario in practical recommender systems. One way to solve this task is to use the similarity between item feature vectors of factorization models. By doing so, one may transfer the well-known accuracy of factorization models ob- served at the personalized recommendations to the item-to- item case. This paper introduces context-awareness to item similarities in the factorization framework. Two levels of context-aware similarities are defined and applied to two context-aware implicit feedback based factorization methods (iTALS and iTALSx). We investigate the advantages and drawbacks of the approaches on four real life implicit feedback data sets and we characterize the conditions for their application. The results suggest that it is worth using contextual information for item-to-item recommendations in the factorization framework, however, one should carefully select the appropriate method to achieve similar accuracy gain than in the case of the more general item-to-user recommendation scenario.

Original languageEnglish
Title of host publicationProceedings of the 3rd Workshop on Context-Awareness in Retrieval and Recommendation, CaRR 2013 - In Conjunction with WSDM 2013
Pages19-25
Number of pages7
DOIs
Publication statusPublished - Mar 18 2013
Event3rd Workshop on Context-Awareness in Retrieval and Recommendation, CaRR 2013 - In Conjunction with the 6th ACM International Conference on Web Search and Data Mining, WSDM 2013 - Rome, Italy
Duration: Feb 5 2013Feb 5 2013

Publication series

NameACM International Conference Proceeding Series

Other

Other3rd Workshop on Context-Awareness in Retrieval and Recommendation, CaRR 2013 - In Conjunction with the 6th ACM International Conference on Web Search and Data Mining, WSDM 2013
CountryItaly
CityRome
Period2/5/132/5/13

Keywords

  • Context-awareness
  • Factorization
  • Implicit feedback
  • Item-to-item recommendation
  • Recommender systems

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Hidasi, B., & Tikk, D. (2013). Context-aware item-to-item recommendation within the factorization framework. In Proceedings of the 3rd Workshop on Context-Awareness in Retrieval and Recommendation, CaRR 2013 - In Conjunction with WSDM 2013 (pp. 19-25). (ACM International Conference Proceeding Series). https://doi.org/10.1145/2442670.2442675