ABSTRACT
We propose a fully decentralized collaborative filtering approach that is self-organizing and operates in a distributed way. The relevances between downloading files (items) are stored locally at these items in so called item-based buddy tables and are updated each time that the items are downloaded. We then propose to use the language model to build recommendations for the different users based on the buddy tables of those items a user has downloaded previously. We have tested and compared our distributed collaborative filtering approach to centralized collaborative filtering and showed that it has similar performance. It is therefore a promising technique to facilitate recommendations in peer-to-peer networks.
- J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proc. of UAI, 1998. Google ScholarDigital Library
- T. Hofmann and J. Puzicha. Latent class models for collaborative filtering. In Proc. of IJCAI, 1999. Google ScholarDigital Library
- G. Karypis. Evaluation of item-based top-n recommendation algorithms. In Proc. of the tenth international conference on Information and knowledge management, 2001. Google ScholarDigital Library
- J. Lafferty and C. Zhai. Probabilistic relevance models based on document and query generation. Language Modeling and Information Retrieval, Kluwer International Series on Information Retrieval, V.13, 2003.Google Scholar
- J. Pisson and T. Moors. Survey of research towards robust peer-to-peer networks: search methods. Technical report, Univeristy of New South Wales, 2004.Google Scholar
- J. Wang, M. Reinders, R. Lagendijk, and J. Pouwelse. Distributed collaborative filtering for peer-to-peer file sharing systems. In Eleventh annual conference of the Advanced School for Computing and Imaging, 2005.Google Scholar
Index Terms
- Self-organizing distributed collaborative filtering
Recommendations
Distributed collaborative filtering for peer-to-peer file sharing systems
SAC '06: Proceedings of the 2006 ACM symposium on Applied computingCollaborative filtering requires a centralized rating database. However, within a peer-to-peer network such a centralized database is not readily available. In this paper, we propose a fully distributed collaborative filtering method that is self-...
Trust-based collaborative filtering: tackling the cold start problem using regular equivalence
RecSys '18: Proceedings of the 12th ACM Conference on Recommender SystemsUser-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. This approach is based on finding the most relevant k users from whose rating history we can extract items to recommend. CF, however, suffers ...
Typicality-Based Collaborative Filtering Recommendation
Collaborative filtering (CF) is an important and popular technology for recommender systems. However, current CF methods suffer from such problems as data sparsity, recommendation inaccuracy, and big-error in predictions. In this paper, we borrow ideas ...
Comments