Grouping Like-Minded Users for Ratings’ Prediction

Abstract : Regarding the huge amount of products, sites, information, etc., finding the appropriate need of a user is a very important task. Recommendation Systems (RS) guide users in a personalized way to objects of interest within a large space of possible options. This paper presents an algorithm for recommending movies. We break the recommendation task into two steps: (1) Grouping Like-Minded users, and (2) create model for each group to predict user-movie ratings. In the first step we use the Principal Component Analysis to retrieve latent groups of similar users. In the second step, we employ three different regression algorithms to build models and predict ratings. We evaluate our results against the SVD++ algorithm and validate the results by employing the MAE and RMSE measures. The obtained results show that the algorithm presented gives an improvement in the MAE and the RMSE of about 0.42 and 0.5201 respectively.
Type de document :
Chapitre d'ouvrage
Intelligent Decision Technologies, 2016, 978-3-319-39629-3
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Contributeur : Kamel Smaïli <>
Soumis le : jeudi 23 juin 2016 - 11:22:04
Dernière modification le : mardi 18 décembre 2018 - 16:38:02


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  • HAL Id : hal-01336507, version 1



Soufiene Jaffali, Salma Jamoussi, Abdelmajid Ben Hamadou, Kamel Smaili. Grouping Like-Minded Users for Ratings’ Prediction. Intelligent Decision Technologies, 2016, 978-3-319-39629-3. 〈hal-01336507〉



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