In group recommendation systems, recommendations may be given to arbitrarily composed groups that may not display any particular characteristics across group members. Since individual recommendation systems can assume that the users’ previous behavior is sufficient for coming up with new recommendations, statistical analyses of user logs or user preferences is enough for computing new recommend...

Recommendation systems offer the opportunity for supporting and enhancing a wide variety of activities in requirements engineering. We discuss several potential uses. In particular we highlight the role of recommendation systems in online forums that are used for capturing and discussing feature requests. The recommendation system is used to mitigate problems introduced when face-toface communi...

During recent years, MIMO communication systems become an important research and development subject. Many papers are already published in the fields of channel capacity, synchronization, space time coding, etc. The idea of this book was born during the Wicom Conference in 2007, where many sessions have dedicated to MIMO systems, dealing with physical channel layer as well as protocol aware MIM...

Recommendation systems have been studied actively since the 1990s. Generally, recommendation systems choose one or more candidates from a set of candidates through a filtering process. Methods of filtering can be divided into two categories: collaborative filtering, in which candidates are chosen based on choices of other persons whose interests or tastes are similar, and content-based filterin...

Top-N recommendation systems are useful in many real world applications such as E-commerce platforms. Most previous methods produce top-N recommendations based on the observed user purchase or recommendation activities. Recently, it has been noticed that side information that describes the items can be produced from auxiliary sources and help to improve the performance of top-N recommendation s...

  One of the most famous methods for recommendation is user-based Collaborative Filtering (CF). This system compares active user’s items rating with historical rating records of other users to find similar users and recommending items which seems interesting to these similar users and have not been rated by the active user. As a way of computing recommendations, the ultimate goal of the user-ba...

There are case-based recommender systems that generate personalized recommendations for users exploiting the knowledge contained in past recommendation cases. These systems assume that the quality of a new recommendation depends on the quality of the recorded recommendation cases. In this paper, we present a case model exploited in a mobile critique-based recommender system that generates recom...

Recommender systems use the past experiences and preferences of the target users as a basis to provide personalized recommendations for them and as the same time, solve the information overloading problem. Context as the dynamic information describing the situation of items and users and affecting the user’s decision process is essential to be used by recommender systems. Multidimensional appro...

these days, due to growing the e-commerce sites, access to information about items is easier than past. but because of huge amount of information, we need new filtering techniques to find interested information faster and more accurate. therefore recommender systems (rs) introduced for solving this problem. although several recommender approaches have proposed, collaborative filtering (cf) appr...

Increasing e-commerce data presents new challenges for storing and querying large amounts of data to online recommendation systems. Recent studies on recommendation systems show that graph data model is more efficient than relational data model for processing complex data. This paper proposes a new graph data storage model for the collaborative filtering-based recommendation system. We present ...