Item Recommendation
msra(2010)
摘要
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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