Exploiting Semantic and Social Information in Recommendation Algorithms
| Autor: | Dominique Laurent, Dalia Sulieman, Hubert Kadima, Maria Malek |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Measure (data warehouse)
Computer science 02 engineering and technology Recommender system computer.software_genre Data set 020204 information systems 0202 electrical engineering electronic engineering information engineering Collaborative filtering 020201 artificial intelligence & image processing Probabilistic analysis of algorithms Data mining Semantic relevance Social information Social network analysis computer Algorithm |
| Zdroj: | Communications in Computer and Information Science ISBN: 9783642401398 ISIP |
| DOI: | 10.1007/978-3-642-40140-4_10 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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