The recommendation algorithm based on bipartite network is superior to traditional methods on accuracy and diversity, which proves that considering the network topology of recommendation systems could help us to improve recommendation results. However, existing algorithms mainly focus on the overall topology structure and those local characteristics could also play an important role in collabor...

Recommender systems form an extremely diverse body of technologies and approaches. The chapter aims to assist researchers and developers identify the recommendation technology that are most likely to be applicable to different domains of recommendation. Unlike other taxonomies of recommender systems, our approach is centered on the question of knowledge: what knowledge does a recommender system...

Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize competition), other important aspects of recommendation quality, such as the divers...

Recommendation systems have become an important research area. Early recommendation systems were based on collaborative filtering, which uses the principle that if two people enjoy the same product they are likely to have common favorites. We present an alternative recommendation approach based on finding clusters of similar customers using integer programming model which is to find the minimal...

Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical data (via offline evaluation). This paper describes this bias and discuss th...

Social networks have become a new trend for research among computer scientist around the world. Social network had an impact on users' way of life. One of social network usages is recommendation systems. The need of recommendation systems is arising when users try to know best choice for them in many items types (books, experts, locations, technologies, etc.). The problem is that a single perso...

Context-aware recommender systems extend traditional recommender systems by adapting their output to users’ specific contextual situations. Most of the existing approaches to context-aware recommendation involve directly incorporating context into standard recommendation algorithms (e.g., collaborative filtering, matrix factorization). In this paper, we highlight the importance of context simil...

Recently, the next-item/basket recommendation system, which considers the sequential relation between bought items, has drawn attention of researchers. The utilization of sequential patterns has boosted performance on several kinds of recommendation tasks. Inspired by natural language processing (NLP) techniques, we propose a novel neural network (NN) based next-song recommender, CNN-rec, in th...

Recommender systems suggest the most appropriate items to users in order to help customers to find the most relevant items and facilitate sales. Collaborative filtering recommendation algorithm is the most successful technique for recommendation. In view of the fact that collaborative filtering systems depend on neighbors as the source of information, the recommendation quality of this approach...

Today, as the Web is rapidly growing at a faster rate, finding relevant information is becoming extremely difficult. Information or content can be in any form such as music, video, images or text which is of interest to the users. Therefore Recommendation systems come into picture. Recommendation systems are a sub-category of information filtering system that help people find products, correct ...