Abstract
User data scarcity has always been indicated among the major problems of collaborative filtering recommender systems. That is, if two users do not share sufficiently large set of items for whom their ratings are known, then the user-to-user similarity computation is not reliable and a rating prediction for one user can not be based on the ratings of the other. This paper shows that this problem can be solved, and that the accuracy of collaborative recommendations can be improved by: a) partitioning the collaborative user data into specialized and distributed repositories, and b) aggregating information coming from these repositories. This paper explores a content-dependent partitioning of collaborative movie ratings, where the ratings are partitioned according to the genre of the movie and presents an evaluation of four aggregation approaches. The evaluation demonstrates that the aggregation improves the accuracy of a centralized system containing the same ratings and proves the feasibility and advantages of a distributed collaborative filtering scenario.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2007 ACM conference on Recommender systems, RecSys '07 |
| Place of Publication | New York |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 33-40 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781595937308 |
| DOIs | |
| Publication status | Published - 2007 |
| Externally published | Yes |
| Event | ACM Conference on Recommender Systems, RecSys 2007 - Minneapolis, United States Duration: 19 Oct 2007 → 20 Oct 2007 |
Conference
| Conference | ACM Conference on Recommender Systems, RecSys 2007 |
|---|---|
| Country/Territory | United States |
| City | Minneapolis |
| Period | 19/10/07 → 20/10/07 |
Keywords
- Distributed Collaborative Filtering
- Recommender Systems
- Mediation of User Modeling Data