Interest-based Recommendation in Academic Networks using Social Network Analysis
نویسندگان
چکیده
Recommender systems are essential to overcome the information overload problem in professional learning environments. In this paper, we investigate interest-based recommendation in academic networks using social network analytics (SNA) methods. We implemented 21 different recommendation approaches based on traditional Collaborative Filtering (CF), Singular Value Decomposition (SVD)-based RS, Trust-based CF, and SNA-based techniques for recommending new collaborators and research topics to the researchers. The evaluation results show that SNA-based recommendation outperforms traditional CF methods in terms of coverage and thus can provide an effective solution to the sparsity and cold start problems in recommender systems.
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