Published April 28, 2007
| Version 11545
Journal article
Open
MovieReco: A Recommendation System
Authors/Creators
Description
Recommender Systems act as personalized decision
guides, aiding users in decisions on matters related to personal taste.
Most previous research on Recommender Systems has focused on the
statistical accuracy of the algorithms driving the systems, with no
emphasis on the trustworthiness of the user. RS depends on
information provided by different users to gather its knowledge. We
believe, if a large group of users provide wrong information it will
not be possible for the RS to arrive in an accurate conclusion. The
system described in this paper introduce the concept of Testing the
knowledge of user to filter out these "bad users".
This paper emphasizes on the mechanism used to provide robust
and effective recommendation.
Files
11545.pdf
Files
(190.0 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:e84e6c61a837809ff44e8e240f7196a2
|
190.0 kB | Preview Download |
Additional details
References
- Konstan J A, Miller B N, Maltz D, Herlocked J L, Gordon L R and Riedl J GroupLens : Applying Collaborative Filtering to Usenet News Communication ACM 40, 3 (page 77-87)
- Konstan J A, Reidl J, Explaining Collaborative Filtering Reommendation ACM 2000 Conference on Computer Supported Collaborative Work.
- H. Herlocker J, Konstan J A, Borchers Al, Reidl J An Algorithmic Framework for Performing Collaborative Filtering In Proceedings on 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrival,
- Melville P, Mooney J R, Nagarajan R Content Boosted Collaborative Filtering for Improved Recommendations. In 18th National Conference on Artificial Intelligence, Pages 187-192. American Association for Artificial Intelligence, 2002.
- Claypool M, Gokhale A, Miranda T, Murnikov P, Netes D and Sartin M, Combining Content-based and Collaborative Filters in an Online Newspaper. In proceedings of ACM SIGIR Workshop on Recommender Systems 1999J.
- Sarwar M B, Konstan A J, Borchers Al, Herlocker J Miller Brad and Riedl J Using Filtering Agents to Improve Prediction Quality in the Grouplens Research Collaborative Filtering System. In proceedings of the 1998 ACM Conference on Computer Supproted Collaborative Work, Pages 345-354, ACM press 1998
- Basu C, Hirsh H, and Cohen W Recommendation as Classification :Using Social and Content-based Information in Recommendation In Proceedings of the 15th National Conference on Artificial Intelligence, Pages 714-720, American Association for Artificial Intelligence 1998
- Cotter P. and Smyth B 2000 PTV: Intelligent Personalized TV Guides In 12th Conference on Innovative Application of Artificial Intelligence page 957-964
- Pazzani M J 1999 A framework for Collaborative, Content-based and Demographic Filtering. Artificial Intelligence Review 1395-60 page 393-408 [10] Good N, Schafer J B, Konstan J A, Borchers A, Sawar B, Herlocker J, Riedl J Cominin Collaborative Filtering with Personal Agent for Better Recommendation In proceedings of 16th National Conference on Artificial Intelligence (AAAI 99) page 439-446 [11] D Fisk: An Application of Social Information Filtering to Movie Recommendation BT Technol Journal, 14, No 4, page 124-132