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Recommender systems are a reality today. Evaluating recommender systems is difficult because of their extreme diversity. Many aspects need to be considered to be able to benchmark recommender systems against each other. This paper proposes an evaluation framework for content recommender systems which goes beyond traditional prediction accuracy. The first aspects to be considered relate to the input...
Recommender Systems (RS) serve online customers in identifying those items from a variety of choices that best match their needs and preferences. In this context explanations summarize the reasons why a specific item is proposed and strongly increase the users' trust in the system's results. In this paper we propose a framework for generating knowledgeable explanations that exploits domain knowledge...
Social tagging systems pose new challenges to developers of recommender systems. As observed by recent research, traditional implementations of classic recommender approaches, such as collaborative filtering, are not working well in this new context. To address these challenges, a number of research groups worldwide work on adapting these approaches to the specific nature of social tagging systems...
Many modern recommender systems are not suitable for recommending infrequently purchased products such as cars due to lack of user rating data to infrequently purchased products. A big challenge for recommending infrequently purchased products is the lack of data about users' interests. Web log data is an important data resource to derive useful information about users' navigation patterns which in...
This paper presents a successful attempt at evolving web intelligence in the tourism scenario, namely throughout two main areas: User Modeling and Recommender Systems. The first subject deals with the correct modeling of tourists' profiles using a wide variety of techniques, such as stereotypes, keywords and psychological models. These techniques, besides presenting user interests with great coherence...
Information recommender system attempts to present information that is likely to be useful for the user. Showing recommendation reason is an important role of the system. However, current recommender systems give only simple or quantitative reasons for the recommendation. In this paper, we aim at giving precise and non-quantitative reasons which are also easy to understand. We make use of formulas...
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