![]() ![]() ![]() That is to say surprisingly interesting items that they might not have otherwise discovered. The last part of the chapter discusses trends andįuture research which might lead towards the next generation of systems, by describing the role of User Generated ContentĪs a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, Widely adopted techniques for learning user profiles are also presented. The second part of the chapter provides a review of the state of the art of systems adopted in several applicationĭomains, by thoroughly describing both classical and advanced techniques for representing items and user profiles. The first part of the chapter presents theīasic concepts and terminology of contentbased recommender systems, a high level architecture, and their main advantages andĭrawbacks. This chapter provides an overview of content-based recommender systems, with the aim of imposing a degree of order on theĭiversity of the different aspects involved in their design and implementation. Interests are stored, with the attributes of a content object (item), in order to recommend to the user new interesting items. Performed by a content-based recommender consists in matching up the attributes of a user profile in which preferences and Content-based recommendation systems try to recommend items similar to those a given user has liked in the past. Recommender systems have the effect of guiding users in a personalized way to interesting objects in a large space of possible ![]()
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