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Context-Aware Multi-criteria Recommendation Based on Spectral Graph Partitioning

Abstract : 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.
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Submitted on : Friday, December 6, 2019 - 3:18:22 PM
Last modification on : Wednesday, October 28, 2020 - 2:20:04 PM
Long-term archiving on: : Saturday, March 7, 2020 - 5:32:06 PM


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  • HAL Id : hal-02397450, version 1
  • OATAO : 25036


Rime Dridi, Lynda Tamine-Lechani, Yahya Slimani. Context-Aware Multi-criteria Recommendation Based on Spectral Graph Partitioning. International Conference on Database and Expert Systems Applications (DEXA 2019), Aug 2019, Linz, Austria. pp.211-221. ⟨hal-02397450⟩



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