PocketLens: Toward a personal recommender system

被引:146
作者
Miller, BN [1 ]
Konstan, JA [1 ]
Riedl, J [1 ]
机构
[1] Univ Minnesota, Dept Comp Sci & Engn, Minneapolis, MN 55455 USA
关键词
algorithms; measurement; security; collaborative filtering; peer-to-peer networking; recommender systems; privacy;
D O I
10.1145/1010614.1010618
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Recommender 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.
引用
收藏
页码:437 / 476
页数:40
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