PocketLens: Toward a personal recommender system

Bradley N. Miller, Joseph A Konstan, John Riedl

Research output: Contribution to journalArticlepeer-review

232 Scopus citations

Abstract

Recommander systems using collaborative filtering are a popular technique for reducing information overload and finding products to purchase. One limitation of current recommenders is that they are not portable. They can only run on large computers connected to the Internet. A second limitation is that they require the user to trust the owner of the recommender with personal preference data. Personal recommenders hold the promise of delivering high quality recommendations on palmtop computers, even when disconnected from the Internet. Further, they can protect the user's privacy by storing personal information locally, or by sharing it in encrypted form. In this article we present the new PocketLens collaborative filtering algorithm along with five peer-to-peer architectures for finding neighbors. We evaluate the architectures and algorithms in a series of offline experiments. These experiments show that Pocketlens can run on connected servers, on usually connected workstations, or on occasionally connected portable devices, and produce recommendations that are as good as the best published algorithms to date.

Original languageEnglish (US)
Pages (from-to)437-476
Number of pages40
JournalACM Transactions on Information Systems
Volume22
Issue number3
DOIs
StatePublished - Jul 2004

Keywords

  • Collaborative Filtering
  • Peer-to-Peer Networking
  • Privacy
  • Recommender Systems

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