Experiments in Bayesian Recommendation
Barnard, Thomas and Prügel-Bennett, Adam (2011) Experiments in Bayesian Recommendation. In, Proceedings of the 7th Atlantic Web Intelligence Conference, AWIC 2011, Fribourg, Switzerland, January 26-28, 2011. , Springer.
- Accepted Version
The performance of collaborative filtering recommender systems can suffer when data is sparse, for example in distributed situations. In addition popular algorithms such as memory-based collaborative filtering are rather ad-hoc, making principled improvements difficult. In this paper we focus on a simple recommender based on naïve Bayesian techniques, and explore two different methods of modelling probabilities. We find that a Gaussian model for rating behaviour works well, and with the addition of a Gaussian-Gamma prior it maintains good performance even when data is sparse.
|Item Type:||Book Section|
|Keywords:||Recommender systems, Collaborative filtering, Bayesian methods, Naïve Bayes|
|Divisions:||Faculty of Physical and Applied Science > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||02 Dec 2010 23:46|
|Last Modified:||26 Apr 2013 05:04|
|Contributors:||Barnard, Thomas (Author)
Prügel-Bennett, Adam (Author)
|Date:||26 January 2011|
|Further Information:||Google Scholar|
|ISI Citation Count:||0|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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