Group-based recipe recommendations: analysis of data aggregation strategies

Shlomo Berkovsky, Jill Freyne

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

173 Citations (Scopus)

Abstract

Collaborative filtering recommendations were designed primarily for individual user models and recommendations. However, nowadays more and more scenarios evolve, in which the recommended items are consumed by groups of users rather than by individuals. This raises the need to uncover the most appropriate group-based collaborative filtering recommendation strategy. In this work we investigate the use of aggregated group data in collaborative filtering recipe recommendations. We present results of a study that exploits recipe ratings provided by families of users, in order to evaluate the accuracy of several group recommendation strategies and weighting models, and analyze the impact of switching strategies, data aggregation heuristics, and group characteristics on the performance of recommendations.
Original languageEnglish
Title of host publicationProceedings of the fourth ACM conference on Recommender systems, RecSys '10
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages111-118
Number of pages8
ISBN (Electronic)9781605586090
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event4th ACM Conference on Recommender Systems, RecSys 2010 - Barcelona, Spain
Duration: 26 Sep 201030 Sep 2010

Conference

Conference4th ACM Conference on Recommender Systems, RecSys 2010
CountrySpain
CityBarcelona
Period26/09/1030/09/10

Keywords

  • recipe recommendations
  • group recommendations
  • collaborative filtering

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