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Diversity enhancement in community recommendation using tensor decomposition and co-clustering

The major aim of Recommender System is to provide appropriate items for user, based on his preferences and intuitively be assessed with accuracy based metrics like precision and recall. Though, diversity of recommended lists is a new emerging debate in RS evaluation. This work tries to improve diver...

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Main Authors: Mohamed Dahlan, Halina, Che Hussin, Ab. Razak, Koochi, Sareh Rashidi, Koochi, Morteza Rashidi
Format: Article
Published: Asian Research Publishing Network 2015
Subjects:
HF Commerce
Online Access:http://eprints.utm.my/id/eprint/60202/
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id my.utm.60202
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spelling my.utm.602022021-10-06T03:07:08Z http://eprints.utm.my/id/eprint/60202/ Diversity enhancement in community recommendation using tensor decomposition and co-clustering Mohamed Dahlan, Halina Che Hussin, Ab. Razak Koochi, Sareh Rashidi Koochi, Morteza Rashidi HF Commerce The major aim of Recommender System is to provide appropriate items for user, based on his preferences and intuitively be assessed with accuracy based metrics like precision and recall. Though, diversity of recommended lists is a new emerging debate in RS evaluation. This work tries to improve diversity of community recommendation, using membership as main and tag collection as complementary resource. With exploiting Tensor Decomposition and using Latent Semantic Analysis, communities can be represented in latent topics, based on different modes including member-users and tag-collections. As the main contribution, this work applies diversification on recommended list in different modes, based on intuitive idea that, communities can be differ from different points of view such as membership, or tag collections. Experimental results accomplished on a Flicker dataset show the meaningful improvement in aggregate diversity (for the system) with less accuracy-loss comparing to current methods; moreover it also shows improvements in intra-list diversity (for single user) which is neglected in previous works. As a result, clustering the communities with similar users, or tags, gives the opportunity to diversify the recommended lists to cover more diverse communities with different member users, or different tag content, and this multi-mode diversity lead to better list for user and better coverage for system. Asian Research Publishing Network 2015 Article PeerReviewed Mohamed Dahlan, Halina and Che Hussin, Ab. Razak and Koochi, Sareh Rashidi and Koochi, Morteza Rashidi (2015) Diversity enhancement in community recommendation using tensor decomposition and co-clustering. Journal Of Theoretical And Applied Information Technology, 82 (2). pp. 221-229. ISSN 1992-8645
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic HF Commerce
spellingShingle HF Commerce
Mohamed Dahlan, Halina
Che Hussin, Ab. Razak
Koochi, Sareh Rashidi
Koochi, Morteza Rashidi
Diversity enhancement in community recommendation using tensor decomposition and co-clustering
description The major aim of Recommender System is to provide appropriate items for user, based on his preferences and intuitively be assessed with accuracy based metrics like precision and recall. Though, diversity of recommended lists is a new emerging debate in RS evaluation. This work tries to improve diversity of community recommendation, using membership as main and tag collection as complementary resource. With exploiting Tensor Decomposition and using Latent Semantic Analysis, communities can be represented in latent topics, based on different modes including member-users and tag-collections. As the main contribution, this work applies diversification on recommended list in different modes, based on intuitive idea that, communities can be differ from different points of view such as membership, or tag collections. Experimental results accomplished on a Flicker dataset show the meaningful improvement in aggregate diversity (for the system) with less accuracy-loss comparing to current methods; moreover it also shows improvements in intra-list diversity (for single user) which is neglected in previous works. As a result, clustering the communities with similar users, or tags, gives the opportunity to diversify the recommended lists to cover more diverse communities with different member users, or different tag content, and this multi-mode diversity lead to better list for user and better coverage for system.
format Article
author Mohamed Dahlan, Halina
Che Hussin, Ab. Razak
Koochi, Sareh Rashidi
Koochi, Morteza Rashidi
author_facet Mohamed Dahlan, Halina
Che Hussin, Ab. Razak
Koochi, Sareh Rashidi
Koochi, Morteza Rashidi
author_sort Mohamed Dahlan, Halina
title Diversity enhancement in community recommendation using tensor decomposition and co-clustering
title_short Diversity enhancement in community recommendation using tensor decomposition and co-clustering
title_full Diversity enhancement in community recommendation using tensor decomposition and co-clustering
title_fullStr Diversity enhancement in community recommendation using tensor decomposition and co-clustering
title_full_unstemmed Diversity enhancement in community recommendation using tensor decomposition and co-clustering
title_sort diversity enhancement in community recommendation using tensor decomposition and co-clustering
publisher Asian Research Publishing Network
publishDate 2015
url http://eprints.utm.my/id/eprint/60202/
_version_ 1715189652130889728
score 13.223943

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