|Title:||Leveraging tagging data for recommender systems|
|Abstract:||The goal of recommender systems is to provide personalized recommendations of products or services to users facing the problem of information overload on the Web. They provide personalized recommendations that best suit a customer's taste, preferences, and individual needs. Especially on large-scale Web sites where millions of items such as books or movies are offered to the users, recommender system technologies play an increasingly important role. One of their main advantages is that they reduce a user's decision-making effort. However, recommender systems are also of high importance from the service provider or system perspective. For instance, they can convince a customer to buy something or develop trust in the system as a whole which ensures customer loyalty and repeat sales gains. With the advent of the Social Web, user generated content has enriched the social dimension of the Web. New types of Web applications have emerged which emphasize content sharing and collaboration. These so-called Social Web platforms turned users from passive recipients of information into active and engaged contributors. As a result, the amount of user contributed information provided by the Social Web poses both new possibilities and challenges for recommender system research. This work deals with the question of how user-provided tagging data can be used to improve the quality of recommender systems. Tag-based recommendations and explanations are the two main areas of contribution in this work. The area of tag-based recommendations deals mainly with the topic of recommending items by exploiting tagging data. A tag recommender algorithm is proposed which can generate highly-accurate tag recommendations in real-time. Furthermore, the concept of user- and item-specific tag preferences is introduced in this work. By attaching feelings to tags users are provided a powerful means to express in detail which features of an item they particularly like or dislike. Additionally, new recommendation schemes are presented that can exploit tag preference data to improve recommendation accuracy. The area of tag-based explanations, on the other hand, deals with questions of how explanations for recommendations should be communicated to the user in the best possible way. New explanation methods based on personalized and non-personalized tag clouds are introduced. The personalized tag cloud interface makes use of the idea of user- and item-specific tag preferences. Furthermore, a first set of possible guidelines for designing or choosing an explanation interface for a recommender system is provided.|
|Appears in Collections:||LS 13 Dienstleistungsinformatik|
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