Name
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. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1007/3-540-44795-4_19 | ECML |
Keywords | Field | DocType |
user rating,user preference,recommender system,personalized recommendation,accurate prediction,recommendation list,automatic decomposition,interest group,novel collaborative,model-based framework,probabilistic model,collaborative filtering | Recommender system,Collaborative filtering,Computer science,Matrix decomposition,Statistical model,Artificial intelligence,Probabilistic logic,Sparse matrix,Machine learning,Computation,Scalability | Conference |
ISBN | Citations | PageRank |
3-540-42536-5 | 22 | 7.09 |
References | Authors | |
11 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Hofmann | 1 | 30 | 8.97 |