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A core value in recommender systems is personalization, the idea that the recommendations produced are those that match the user's preferences. However, in many real-world recommendation contexts, the concerns of additional stake-holders may come into play, such as the producers of items or those of the system owner. Some researchers have examined special(More)
Recommender systems help users to find interesting items when there are many possibilities. They have been used in many domains such as movies, music, news articles, books etc. Recently there has been an increasing interest in using recommender systems in educational systems where the goal of the recommender system is to help users to find the most relevant(More)
Recommender systems are typically evaluated on their ability to provide items that satisfy the needs and interests of the end user. However, in many real world applications, users are not the only stakeholders involved. There may be a variety of individuals or organizations that benefit in different ways from the delivery of recommendations. In this paper,(More)
Number of services that banks offer customers, has been increased significantly in recent years. There is a variety of services available through the different channels like internet bank, mobile bank, telephone bank, ATM and so forth. The need to personalize these services for customers is felt more than before since people are busier than ever and have a(More)
Collaborative filtering (CF) recommender systems are typically unable to generate adequate recommendations in sparse datasets. Empirical evidence suggests that incorporation of a trust network among the users of a recommender system can significantly help to alleviate this problem. For this reason, some studies have been done on combining CF with(More)
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