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Biclustering Based on FCA and Partition Pattern Structures for Recommendation Systems

Nyoman Juniarta 1 Victor Codocedo 2 Miguel Couceiro 1 Amedeo Napoli 1
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
2 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : This paper focuses on item recommendation for visitors in a museum within the framework of European Project CrossCult about cultural heritage. We present a theoretical research work about recommendation using bicluster-ing. Our approach is based on biclustering using FCA and partition pattern structures. We investigate the possibility of incorporating the order information using this approach. Then, given the dataset of visitor trajectories, the result of our biclustering can be used to build a collaborative recommendation system.
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Submitted on : Friday, October 5, 2018 - 9:32:57 PM
Last modification on : Wednesday, November 3, 2021 - 7:09:17 AM
Long-term archiving on: : Sunday, January 6, 2019 - 5:16:19 PM


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  • HAL Id : hal-01889309, version 1


Nyoman Juniarta, Victor Codocedo, Miguel Couceiro, Amedeo Napoli. Biclustering Based on FCA and Partition Pattern Structures for Recommendation Systems. SFC 2018 - XXVèmes Rencontres de la Société Francophone de Classification, Sep 2018, Paris, France. ⟨hal-01889309⟩



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