Learning What People (Don’t) Want
Identifieur interne : 002233 ( Main/Exploration ); précédent : 002232; suivant : 002234Learning What People (Don’t) Want
Auteurs : Thomas Hofmann [États-Unis]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2001.
Abstract
Abstract: Recommender systems make use of a database of user ratings to generate personalized recommendations and help people to find relevant products, items, or documents. In this paper, we present a probabilistic, model-based framework for user ratings based on a novel collaborative filtering technique that performs an automatic decomposition of user preferences. Our approach has several benefits, including highly accurate predictions, task-optimized model learning, mining of interest groups and patterns, as well as a highly efficient and scalable computation of predictions and recommendation lists.
Url:
DOI: 10.1007/3-540-44795-4_19
Affiliations:
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