A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity
作者： Vinti Agarwal , K. K. Bharadwaj
DOI: 10.1007/S13278-012-0083-7
关键词: Set (psychology) 、 Information retrieval 、 Information overload 、 Machine learning 、 Artificial intelligence 、 Domain (software engineering) 、 Similarity (psychology) 、 Collaborative filtering 、 Preference 、 Evolutionary algorithm 、 Computer science 、 Missing data 、 Media Technology 、 Human-Computer Interaction 、 Communication 、 Information Systems 、 Computer Science Applications
摘要: The tremendous growth in the amount of attention and users, on social networking sites (SNSs), has led to information overload and that adds to the difficulty of making accurate recommendations of new friends to the users of SNSs. This article incorporates collaborative filtering (CF), the most successful and widely used filtering technique, in social networks to facilitate users in exploring new friends having similar interests while being connected with old ones as well. Here, first we design an implicit rating model, for estimating a user's affinity …
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