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2016 | OriginalPaper | Chapter

Grouping Like-Minded Users for Ratings’ Prediction

Authors : Soufiene Jaffali, Salma Jamoussi, Abdelmajid Ben Hamadou, Kamel Smaili

Published in: Intelligent Decision Technologies 2016

Publisher: Springer International Publishing

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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.

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next chapter An Approach for Designing Order Size Dependent Lead Time Models for Use in Inventory and Supply Chain Management
Footnotes
7
The network around a single node (ego).
 
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Metadata
Title
Grouping Like-Minded Users for Ratings’ Prediction
Authors
Soufiene Jaffali
Salma Jamoussi
Abdelmajid Ben Hamadou
Kamel Smaili
Copyright Year
2016
Book
Intelligent Decision Technologies 2016

Print ISBN: 978-3-319-39629-3

Electronic ISBN: 978-3-319-39630-9

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-39630-9_1

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