Collaborative and content-based filtering for item recommendation on social bookmarking websites

Toine Bogers, Antal Van Den Bosch

Research output: Contribution to journalConference article in JournalResearchpeer-review

27 Citations (Scopus)

Abstract

Social bookmarking websites allow users to store, organize, and search bookmarks of web pages. Users of these services can annotate their bookmarks by using informal tags and other metadata, such as titles, descriptions, etc. In this paper, we focus on the task of item recommendation for social bookmarking websites, i.e. predicting which unseen bookmarks a user might like based on his or her profile. We examine how we can incorporate the tags and other metadata into a nearest-neighbor collaborative filtering (CF) algorithm, by replacing the traditional usage-based similarity metrics by tag overlap, and by fusing tag-based similarity with usage-based similarity. In addition, we perform experiments with content-based filtering by using the metadata content to recommend interesting items. We generate recommendations directly based on Kullback- Leibler divergence of the metadata language models, and we explore the use of this metadata in calculating user and item similarities. We perform our experiments on three data sets from two different domains: Delicious, CiteULike and BibSonomy.

Original languageEnglish
JournalCEUR Workshop Proceedings
Volume532
Pages (from-to)9-16
Number of pages8
ISSN1613-0073
Publication statusPublished - 1 Dec 2009
Externally publishedYes
EventWorkshop on Recommender Systems and the Social Web, Collocated with the 3rd ACM Conference on Recommender Systems, RecSys 2009 - New York, NY, United States
Duration: 25 Oct 200925 Oct 2009

Conference

ConferenceWorkshop on Recommender Systems and the Social Web, Collocated with the 3rd ACM Conference on Recommender Systems, RecSys 2009
Country/TerritoryUnited States
CityNew York, NY
Period25/10/200925/10/2009

Keywords

  • Collaborative filtering
  • Content-based filtering
  • Folksonomies
  • Recommender systems
  • Social bookmarking

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