GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
The Journal of Machine Learning Research
| Hi-index | 0.00 |
We describe CFW, a computationally efficient algorithm for collaborative filtering that uses posteriors over weights of evidence. In experiments on real data, we show that this method predicts as well or better than other methods in situations where the size of the user query is small. The new approach works particularly well when the user's query contains low frequency (unpopular) items. The approach complements that of dependency networks which perform well when the size of the query is large. Also in this paper, we argue that the use of posteriors over weights of evidence is a natural way to recommend similar items--a task that is somewhat different from the usual collaborative-filtering task.