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- W2894649752 abstract "Recommendation systems are tools of option used to select the data relevant to a given user from online sources. Collaborative filtering (CF) could be the most thriving approach to build recommendation systems and has been employed in many applications. Collaborative filtering algorithms are the much explored techniques within the field of information mining and data retrieval. In CF, users’ past behavior is analyzed so as to determine the connections between the user and their items of interest to suggest an item to the user supported by the opinion of different users with similar liking. CF relies on the actual fact that those customers, who have similar liking within the past, will have similar liking within the future. This paper makes a comprehensive introduction to collaborative filtering, aiming to facilitate readers to grasp this field. First of all, we introduce the background analysis and then try to differentiate between various CF-based social recommendation systems based on the matrix factorization that uses social factors. This paper conjointly describes applications of collaborative filtering in several domains. In our future works, we will utilize user location info (users’ check-ins) to advocate additional personalized and real-time items." @default.
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- W2894649752 date "2018-10-05" @default.
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- W2894649752 title "A Survey on Collaborative Filtering: Tasks, Approaches and Applications" @default.
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- W2894649752 doi "https://doi.org/10.1007/978-981-13-1544-2_24" @default.
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