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2015 | OriginalPaper | Chapter

An Improved Collaborative Recommendation System by Integration of Social Tagging Data

Authors : Sogol Naseri, Arash Bahrehmand, Chen Ding

Published in: Recommendation and Search in Social Networks

Publisher: Springer International Publishing

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Abstract

Recently a lot of research efforts have been spent on building recommender systems by utilizing the abundant online social network data. In this study, we intend to enhance the recommendation accuracy via integrating social networking information with the traditional recommendation algorithms. To achieve this goal, we first propose a new user similarity metric that not only considers tagging activities of users, but also incorporates their social relationships such as friendship and membership, in measuring the closeness of two users. Then we define a new item prediction method which makes use of both user-to-user similarity and item-to-item similarity. Experimental outcomes on Last.fm data produce the positive results that show the accuracy of our proposed approach.

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previous chapter A Comprehensive Analysis of Detection of Online Paid Posters

next chapter Personalization of Web Search Using Social Signals

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http://www.di.unito.it/~schifane/dataset_lastfm_WSDM.zip.

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Title: An Improved Collaborative Recommendation System by Integration of Social Tagging Data
Authors: Sogol Naseri
Arash Bahrehmand
Chen Ding
Publisher: Springer International Publishing
Book: Recommendation and Search in Social Networks
Print ISBN: 978-3-319-14378-1

Electronic ISBN: 978-3-319-14379-8

Copyright Year: 2015
DOI: https://doi.org/10.1007/978-3-319-14379-8_7

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