Semantic Pattern Mining Based Web Service Recommendation

Hafida Naim (1) , Mustapha Aznag (1) , Nicolas Durand (1) , Mohamed Quafafou (1)

1 LSIS - Laboratoire des Sciences de l'Information et des Systèmes

This paper deals with the problem of web service recommendation. We propose a new content-based recommendation system. Its originality comes from the combination of probabilistic topic models and pattern mining to capture the maximal common semantic of sets of services. We define the notion of semantic patterns which are maximal frequent itemsets of topics. In the off-line process, the computation of these patterns is performed by using frequent concept lattices in order to find also the sets of services associated to the semantic patterns. These sets of services are then used to recommend services in the on-line process. We compare the results of the proposed system in terms of precision and normalized discounted cumulative gain with Apache Lucene and SAWSDL-MX2 Matchmaker on real-world data. Our proposition outperforms these two systems.

Mots clés: Web services; Recommendation; Topic models; Formal concept analysis; Concept lattice; Maximal frequent itemsets

Domaines: Informatique [cs]

Hafida Naim, Mustapha Aznag, Nicolas Durand, Mohamed Quafafou. Semantic Pattern Mining Based Web Service Recommendation. IEEE 14th International Conference on Service Oriented Computing (ICSOC 2016), 2016, Banff, Alberta, Canada. pp.417-432. ⟨hal-01465113⟩