A Random Walk Model for Item Recommendation in Social Tagging Systems
ACM Transactions on Management Information Systems (TMIS)
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Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper explains the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, a system that combines content boosted recommendation and collaborative Filtering to recommend restaurants.