Over the past two decades, a large amount of research effort has been devoted to
developing algorithms that generate recommendations. The resulting research progress has …

Collaborative Filtering (CF) computes recommendations by leveraging a historical data set
of users' ratings for items. CF assumes that the users' recorded ratings can help in predicting …

Context-aware recommendation (CAR) can lead to significant improvements in the
relevance of the recommended items by modeling the nuanced ways in which context …

User and item splitting are well-known approaches to context-aware recommendation. To
perform item splitting, multiple copies of an item are created based on the contexts in which …

Nowadays recommender systems are successfully used in various fields. One application is
the recommendation of restaurants, where even if the method of customer service is the …

Rich contextual information is typically available in many recommendation domains allowing
recommender systems to model the subtle effects of context on preferences. Most contextual …

Location-based social networks (LBS) enable users to checkin at points of interests (POIs),
share this information with other users within the network, and receive recommendations …

Collaborative Filter is proved to be effective in recommendations and widely used in the
recommender system for online stores. The mechanism of this method is to find similarities …

The architecture and basic models of a context-aware recommendation system based on
collaborative filtering are proposed. The major problems in creating such systems are …

The development of information technologies raises the problem of information overload.
Recommender systems aim to choose the best application or content from numerous …