Abstract
In this paper we present algorithms for recommender systems. Our algorithms rely on a semantic relevance measure and a social network analysis measure to partially explore the network using depth-first search and breath-first search strategies. We apply these algorithms to a real data set and we compare them with item-based collaborative filtering and hybrid recommendation algorithms. Our experiments show that our algorithms outperform existing recommendation algorithms, while providing good precision and F-measure results.
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Sulieman, D., Malek, M., Kadima, H., Laurent, D. (2013). Exploiting Semantic and Social Information in Recommendation Algorithms. In: Tanaka, Y., Spyratos, N., Yoshida, T., Meghini, C. (eds) Information Search, Integration and Personalization. ISIP 2012. Communications in Computer and Information Science, vol 146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40140-4_10
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DOI: https://doi.org/10.1007/978-3-642-40140-4_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40139-8
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