Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments

Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid systems. We propose a unified probabilistic framework for ...

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 ...

Recently, music recommender systems have been proposed to help users obtain the interested music. Traditional recommender systems making attempts to discover users' musical preferences by ratings always suffer from problems of rating diversity, rating ...