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Recommender systems represent a popular area of personalization technologies that has enjoyed a tremendous amount of research and development activity in both academia and industry in the last 10–15 years. Recommender systems research typically explores and develops techniques and applications for recommending various products or services to individual users based on the knowledge of users’ tastes and preferences as well as users’ past activities (such as previous purchases), which are applicable in a variety of domains and settings (Jannach et al. 2010). While a substantial amount of research has already been performed in the area of recommender systems, many existing approaches have focused on recommending the most relevant items to users and do not take into account any additional contextual information, such as time, location, weather, the user’s current goals, the user’s mood, presence of other people, or the type of the device through which the recommendation is consumed. In other words, traditionally recommender systems deal with applications having only two types of entities, users and items, and do not put them into a context when providing recommendations. However, the importance of contextual information has been recognized by researchers and practitioners in many

Preface to the special issue on context-aware recommender systems