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...d continuous contextual domains (see Section 4). Context plays an important role in determining users’ behavior by providing additional information that can be exploited in building predictive models =-=[2]-=-. Context-aware recommender systems is a new area of research [1]. The approaches can be classified into three main groups: prefiltering, post-filtering and contextual modeling [2]. The user micro-pro...

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...hown to provide more accurate rating predictions [7, 10, 6] and more relevant recommendations [5]. CARS approaches can be divided into: contextual prefiltering; post-filtering and contextual modeling =-=[2]-=-. The earlier research concentrated on pre-filtering approaches. For example, in the classical reduction based CACF approach [1], first the target user context is identified and consequently only the ...

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...ee which strategy is best. Also, more research is needed to establish when to regard a criterion as ”unimportant”. The issue of multiple criteria is also the topic of another chapter in this handbook =-=[1]-=-. 7.2 Cold-Start Problem A big problem for recommender systems is the so-called cold-start problem: to adapt to a user, the system needs to know what the user liked in the past. This is needed in cont...

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...re suggesting a music track to be played in her car. Context-aware recommender systems (CARSs) are gaining ever more attention and various techniques have been introduced to improve their performance =-=[1]-=-. C. Huemer and T. Setzer (Eds.): EC-Web 2011, LNBIP 85, pp. 89–100, 2011. c○ Springer-Verlag Berlin Heidelberg 201190 L. Baltrunas et al. To adapt recommendations to the user’s context, the dependen...

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...and techniques providing suggestions for items to be of use to a user [8]. It is a matter of fact that more compelling and useful recommendations can be identified if the context of the user is known =-=[1]-=-. Here we adopt the definition of context provided by [5]: context is “any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is cons...

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... set of recommender systems that incorporate contextual information in the recommendation process. As outlined by the context-aware recommendation community, much work is needed to advance this field =-=[3]-=-. We discuss the challenges that this community has identified in recent years, as well as how these challenges can be tackled for the TEL field. The article is organized as follows: first, we present...

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...r to redistribute to lists, requires prior specific permission and/or a fee. SAC’14 March 24-28, 2014, Gyeongju, Korea. Copyright 2014 ACM 978-1-4503-2469-4/14/03 ...$15.00. resulting recommendations =-=[1]-=-. The context-aware approach has been demonstrated to be effective in several domains [4, 12, 7, 16]. One popular and efficient technique for context-aware recommendation is item splitting [4, 5], whe...

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...staurants desirable for a dinner date is not the same as for a quick business lunch. This insight is the motivation behind the expansion of recent research in context-aware recommender systems (CARS) =-=[3,4,2]-=-. A number of researchers have shown that appropriate application of context can create effective recommendation solutions in a variety of domains [5,7,10,15]. One of the key problems in CARS research...

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...recommender systems try to additionally incorporate contexts to estimate user preferences, which turns the prediction into a “multidimensional” rating function – R: Users × Items × Contexts → Ratings =-=[2]-=-. Adomavicius, et al. [2] introduce a two-part classification of contextual information. Their taxonomy is based on two considerations: what a recommender system knows about a contextual factor and ho...