1. Context-Aware Multi-criteria Recommendation Based on Spectral Graph Partitioning
- Author
-
Yahya Slimani, Rime Dridi, Lynda Tamine, Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Université de la Manouba - UMA (TUNISIA), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Université de Carthage (TUNISIA), Institut de Recherche en Informatique de Toulouse - IRIT (Toulouse, France), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Laboratoire d'Informatique pour les Systèmes Industriels (LISI), Faculté des Sciences Mathématiques, Physiques et Naturelles de Tunis (FST), Université de Tunis El Manar (UTM)-Université de Tunis El Manar (UTM), Université de la Manouba [Tunisie] (UMA), Recherche d’Information et Synthèse d’Information (IRIT-IRIS), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), and Université Fédérale Toulouse Midi-Pyrénées
- Subjects
050101 languages & linguistics ,Multi-criteria ,[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB] ,Information retrieval ,Computer science ,05 social sciences ,Graph partition ,Context ,02 engineering and technology ,Recommender system ,Graph ,Multi criteria ,Base de données ,0202 electrical engineering, electronic engineering, information engineering ,Graph (abstract data type) ,020201 artificial intelligence & image processing ,0501 psychology and cognitive sciences ,Cluster analysis - Abstract
International audience; Both multi-criteria recommendation and context-aware recommendation are well addressed in previous research but separately in most of existing work. In this paper, we aim to contribute to the under-explored research problem which consists in tailoring the multi-criteria rating predictions to users involved in specific contexts. We investigate the application of simultaneous clustering based on the application of a spectral partitioning graph method over situational contexts in the one hand and criteria in the other hand. Besides, we conjecture that even with similar criteria-related ratings, the importance of criteria might differ among users. This idea leads us to use prioritized aggregation operators as means of multi-criteria rating aggregations. Our experimental results on a real-world dataset show the effectiveness of our approach.
- Published
- 2019