Author(s): Mohammad Julashokri | Mohammad Fathian | Mohammad Reza Gholamian | Ahmad Mehrbod
Journal: Management Science Letters
ISSN 1923-9335
Volume: 1;
Issue: 4;
Start page: 449;
Date: 2011;
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Keywords: Recommender systems Customer preference | Collaborative filtering | Customer profile | Group preferences
ABSTRACT
Recommender systems are tools for realization one to one marketing. Recommender systems are systems, which attract, retain, and develop customers. Recommender systems use several ways to make recommendations. Two ways are using more than the others: collaborative filtering and content-based filtering. In this study, a recommender system model based on collaborative filtering has proposed. Proposed model was endeavored to improve the customer profile in collaborative systems to enhance the recommender system efficiency. This improvement was done using time context and group preferences. Experimental results show that the proposed model has a better recommendation performance than existing models.
Journal: Management Science Letters
ISSN 1923-9335
Volume: 1;
Issue: 4;
Start page: 449;
Date: 2011;
VIEW PDF
DOWNLOAD PDF Original pageKeywords: Recommender systems Customer preference | Collaborative filtering | Customer profile | Group preferences
ABSTRACT
Recommender systems are tools for realization one to one marketing. Recommender systems are systems, which attract, retain, and develop customers. Recommender systems use several ways to make recommendations. Two ways are using more than the others: collaborative filtering and content-based filtering. In this study, a recommender system model based on collaborative filtering has proposed. Proposed model was endeavored to improve the customer profile in collaborative systems to enhance the recommender system efficiency. This improvement was done using time context and group preferences. Experimental results show that the proposed model has a better recommendation performance than existing models.
