Improving New User Recommendations with Rule-based Induction on Cold User Data

Abstract : With recommender systems, users receive items recommended on the basis of their profile. New users experience the cold start problem: as their profile is very poor, the system performs very poorly. In this paper, classical new user cold start techniques are improved by exploiting the cold user data, i.e. the user data that is readily available (e.g. age, occupation, location, etc.), in order to automatically associate the new user with a better first profile. Relying on the existing alpha-community spaces model, a rule-based induction process is used and a recommendation process based on the "level of agreement" principle is defined. The experiments show that the quality of recommendations compares to that obtained after a classical new user technique, while the new user effort is smaller as no initial ratings are asked.
Type de document :
Communication dans un congrès
ACM Conference on Recommender Systems (RecSys'07), 2007, Minneapolis, MN, United States. pp.121--128, 2007
Liste complète des métadonnées

https://hal.inria.fr/hal-00953877
Contributeur : Marie-Christine Fauvet <>
Soumis le : vendredi 28 février 2014 - 16:03:00
Dernière modification le : mercredi 7 novembre 2018 - 13:42:02

Identifiants

  • HAL Id : hal-00953877, version 1

Collections

Citation

An-Te Nguyen, Nathalie Denos, Catherine Berrut. Improving New User Recommendations with Rule-based Induction on Cold User Data. ACM Conference on Recommender Systems (RecSys'07), 2007, Minneapolis, MN, United States. pp.121--128, 2007. 〈hal-00953877〉

Partager

Métriques

Consultations de la notice

170