Hybrid Weighting Schemes For Collaborative Filtering

Afshin Moin 1 Claudia-Lavinia Ignat 1
1 SCORE - Services and Cooperation
Inria Nancy - Grand Est, LORIA - NSS - Department of Networks, Systems and Services
Abstract : Neighborhood based algorithms are one of the most common approaches to Collaborative Filtering (CF). The core element of these algorithms is similarity computation between items or users. It is reasonable to assume that some ratings of a user bear more information than others. Weighting the ratings proportional to their importance is known as feature weighting. Nevertheless in practice, none of the existing weighting schemes results in significant improvement to the quality of recommendations. In this paper, we suggest a new weighting scheme based on Matrix Factorization (MF). In our scheme, the importance of each rating is estimated by comparing the coordinates of users (items) taken from a latent feature space computed through Matrix Factorization (MF). Moreover, we review the effect of a large number of weighting schemes on item based and user based algorithms. The effect of various influential parameters is studied running extensive simulations on two versions of the Movielens dataset. We will show that, unlike the existing weighting schemes, ours can improve the performance of CF algorithms. Furthermore, their cascading capitalizes on each other's improvement.
Type de document :
[Research Report] INRIA Nancy. 2014
Liste complète des métadonnées

Littérature citée [18 références]  Voir  Masquer  Télécharger

Contributeur : Afshin Moin <>
Soumis le : lundi 19 janvier 2015 - 15:39:18
Dernière modification le : jeudi 11 janvier 2018 - 06:23:13
Document(s) archivé(s) le : lundi 20 avril 2015 - 11:00:42


Fichiers produits par l'(les) auteur(s)


  • HAL Id : hal-00947194, version 2



Afshin Moin, Claudia-Lavinia Ignat. Hybrid Weighting Schemes For Collaborative Filtering. [Research Report] INRIA Nancy. 2014. 〈hal-00947194v2〉



Consultations de la notice


Téléchargements de fichiers