Paper Info
Title
Modeling item selection and relevance for accurate recommendations: a bayesian approach
Abstract
We propose a bayesian probabilistic model for explicit preference data. The model introduces a generative process, which takes into account both item selection and rating emission to gather into communities those users who experience the same items and tend to adopt the same rating pattern. Each user is modeled as a random mixture of topics, where each topic is characterized by a distribution modeling the popularity of items within the respective user-community and by a distribution over preference values for those items. The proposed model can be associated with a novel item-relevance ranking criterion, which is based both on item popularity and user's preferences. We show that the proposed model, equipped with the new ranking criterion, outperforms state-of-art approaches in terms of accuracy of the recommendation list provided to users on standard benchmark datasets.
Year
DOI
Venue
2011
10.1145/2043932.2043941
RecSys
Keywords
Field
DocType
item popularity,bayesian probabilistic model,rating emission,novel item-relevance ranking criterion,accurate recommendation,item selection,rating pattern,preference value,bayesian approach,new ranking criterion,explicit preference data,probabilistic model,recommender systems,topic models,collaborative filtering,recommender system
Data mining,Computer science,Popularity,Artificial intelligence,Recommender system,Collaborative filtering,Information retrieval,Ranking,Statistical model,Generative grammar,Topic model,Machine learning,Bayesian probability
Conference
Citations 
PageRank 
References 
7
0.48
12
Authors
4
Name
Order
Citations
PageRank
Nicola Barbieri151129.53
Gianni Costa223524.04
Giuseppe Manco391868.94
Riccardo Ortale428227.46