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An Innovative Combinatorial Approach for the Spanning Tree Entropy in Flower Network Read chapter Read first chapter

2017 | OriginalPaper | Chapter

A Distributed Recommender System Based on Graded Multi-label Classification

Authors : Khalil Laghmari, Christophe Marsala, Mohammed Ramdani

Published in: Networked Systems

Publisher: Springer International Publishing

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Abstract

Recommender systems are designed to find items in which each user has most likely the highest interest. Items can be of any type such as commercial products, e-learning resources, movies, songs, and jokes. Successful web and mobile applications can collect easily thousands of users, thousands of items, and millions of item ratings in only few months. A solution to store and to process these continuously growing data is to build distributed recommender systems. The challenging task is to find the appropriate distribution strategy allowing an efficient retrieval of needed information. Considering the similarity between the task of predicting a rating, and the task of predicting a membership grade in graded multi-label classification (GMLC), we propose an adapted distribution strategy to efficiently build a decentralized recommender system based on GMLC.

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Footnotes
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Metadata
Title
A Distributed Recommender System Based on Graded Multi-label Classification
Authors
Khalil Laghmari
Christophe Marsala
Mohammed Ramdani
Copyright Year
2017
Book
Networked Systems

Print ISBN: 978-3-319-59646-4

Electronic ISBN: 978-3-319-59647-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59647-1_8

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