Abstract
Recommending content is an important task in many information systems. For example online shopping websites like Amazon give each customer personalized recommendations of products that the user might be interested in. Other examples are video portals like YouTube that recommend videos to visitors. Personalization is attractive both for content providers, who can increase sales or views, and for customers, who can find interesting content more easily. In this chapter, we focus on item recommendation where the task is to create a user-specific ranking for a set of items. Preferences of users about items are learned from the user’s past interaction with the system – e.g. his buying history, viewing history, etc. Thus, the context in item recommenders is the user and user-aware rankings should be generated.
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Rendle, S. (2010). Item Recommendation. In: Context-Aware Ranking with Factorization Models. Studies in Computational Intelligence, vol 330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16898-7_6
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DOI: https://doi.org/10.1007/978-3-642-16898-7_6
Publisher Name: Springer, Berlin, Heidelberg
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