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A Collaborative and Semantic-based Approach for Recommender Systems

Abstract : The constant growth of the Internet has made recommender systems very useful to guide users coping with a large amount of data. In this paper, we present a domain independent collaborative and semantic-based recommender system which uses distinct and complementary modules. The approach targets users with various interests and is based on: (i) a collaborative module using association rules in order to mine a set of rules for the target user, (ii) a semantic module using the domain ontology to reason about items, (iii) a frequency module using the frequency of the item features in order to discover additional items to be recommended. Unlike numerical approaches, applying these different modules separately provides a multi-view basis to explain the recommendations proposed to the user. Our recommendation system has been tried out on the MovieLens dataset and the results that have been evaluated by a set of volunteers are promising.
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Conference papers
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https://hal-supelec.archives-ouvertes.fr/hal-00545850
Contributor : Evelyne Faivre <>
Submitted on : Monday, December 13, 2010 - 10:05:25 AM
Last modification on : Wednesday, June 20, 2018 - 2:32:02 PM

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  • HAL Id : hal-00545850, version 1

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Roza Lemdani, Nacéra Bennacer Seghouani, Géraldine Polaillon, Yolaine Bourda. A Collaborative and Semantic-based Approach for Recommender Systems. 10th International Conference on Intelligent Systems Design and Applications (ISDA'10), Nov 2010, Caire, Egypt. pp.469-476. ⟨hal-00545850⟩

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