Using Collaborative Filtering to Enhance Domain-Independent CBR Recommender's Personalization - Archive ouverte HAL Access content directly
Conference Papers Year : 2015

Using Collaborative Filtering to Enhance Domain-Independent CBR Recommender's Personalization

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Jihane Karim
  • Function : Author
  • PersonId : 1060409
Raja Chiky
  • Function : Author
  • PersonId : 1060410
Michel Manago
  • Function : Author
  • PersonId : 1060411

Abstract

Case-Based Reasoning (CBR) is a problem solving methodology that reuses the knowledge of past experiences to solve new problems. It's a knowledge-based technique that has been introduced to the recommendation field to allow reasoning on domain knowledge and to generate more accurate recommendations. If CBR helps suggesting items that meet the users' search criteria, it has the disadvantage of being domain-dependent (all the reasoning process is generally based on hard-coded domain knowledge) and generating less personalized recommendations. In this paper, we propose an approach for a generic and personalized CBR-based recommender system. First, we use a generic ontology to formalize all the knowledge required during the reasoning process. The ontology represents an intermediate layer between the recommender engine and the application domain to ensure the domain-independence criteria. Second, we propose a hybridization strategy that combines CBR and collaborative filtering to alleviate the limitations of CBR and improve the personalized character of the recommendations. Finally, preliminary validation is performed using a publicly available data set of restaurants.
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Dates and versions

hal-02402483 , version 1 (10-12-2019)

Identifiers

  • HAL Id : hal-02402483 , version 1

Cite

Jihane Karim, Matthieu Manceny, Raja Chiky, Michel Manago, Marie-Aude Aufaure. Using Collaborative Filtering to Enhance Domain-Independent CBR Recommender's Personalization. IEEE 9th International Conference on Research Challenges in Information Science (RCIS), May 2015, Athens, Greece. ⟨hal-02402483⟩
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