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Using Tags for Measuring the Semantic Similarity of Users to Enhance Collaborative Filtering Recommender Systems

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@article{IJASEIT1826,
   author = {Ayman S. Ghabayen and Shahrul Azman Mohd Noah},
   title = {Using Tags for Measuring the Semantic Similarity of Users to Enhance Collaborative Filtering Recommender Systems},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {6},
   year = {2017},
   pages = {2063--2070},
   keywords = {recommendation system; collaborative filtering; social tagging system},
   abstract = {Recent years have seen a significant growth in social tagging systems, which allow users to use their own generated tags to organize, categorize, describe and search digital content on social media. The growing popularity of tagging systems is leading to an increasing need for automatic generation of recommended items for users. Much previous research focuses on incorporating recommender techniques in social tagging systems to support the suggestion of suitable tags for annotating related items. Collaborative filtering is one such technique. The most critical task in collaborative filtering is finding related users with similar preferences, i.e., “liked-minded” users. Despite the popularity of collaborative filtering, it still suffers from certain limitations in relation to “cold-start” users, for example, where often there are insufficient preferences to make recommendations. Moreover, there is the data-sparsity problem, where there is limited user feedback data to identify similarities in users’ interests because there is no intersection between users’ transactional data a situation which also results in degraded recommendation quality. For this reason, in this paper we present a new collaborative filtering approach based on users’ semantic tags, which calculates the similarity between users by discovering the semantic spaces in their posted tags. We believe that this approach better reflects the semantic similarity between users according to their tagging perspectives and consequently improves recommendations through the identification of semantically related items for each user. Our experiment on a real-life dataset shows that the proposed approach outperforms the traditional user-based collaborative filtering approach in terms of improving the quality of recommendations.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1826},
   doi = {10.18517/ijaseit.7.6.1826}
}

EndNote

%A Ghabayen, Ayman S.
%A Noah, Shahrul Azman Mohd
%D 2017
%T Using Tags for Measuring the Semantic Similarity of Users to Enhance Collaborative Filtering Recommender Systems
%B 2017
%9 recommendation system; collaborative filtering; social tagging system
%! Using Tags for Measuring the Semantic Similarity of Users to Enhance Collaborative Filtering Recommender Systems
%K recommendation system; collaborative filtering; social tagging system
%X Recent years have seen a significant growth in social tagging systems, which allow users to use their own generated tags to organize, categorize, describe and search digital content on social media. The growing popularity of tagging systems is leading to an increasing need for automatic generation of recommended items for users. Much previous research focuses on incorporating recommender techniques in social tagging systems to support the suggestion of suitable tags for annotating related items. Collaborative filtering is one such technique. The most critical task in collaborative filtering is finding related users with similar preferences, i.e., “liked-minded” users. Despite the popularity of collaborative filtering, it still suffers from certain limitations in relation to “cold-start” users, for example, where often there are insufficient preferences to make recommendations. Moreover, there is the data-sparsity problem, where there is limited user feedback data to identify similarities in users’ interests because there is no intersection between users’ transactional data a situation which also results in degraded recommendation quality. For this reason, in this paper we present a new collaborative filtering approach based on users’ semantic tags, which calculates the similarity between users by discovering the semantic spaces in their posted tags. We believe that this approach better reflects the semantic similarity between users according to their tagging perspectives and consequently improves recommendations through the identification of semantically related items for each user. Our experiment on a real-life dataset shows that the proposed approach outperforms the traditional user-based collaborative filtering approach in terms of improving the quality of recommendations.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1826
%R doi:10.18517/ijaseit.7.6.1826
%J International Journal on Advanced Science, Engineering and Information Technology
%V 7
%N 6
%@ 2088-5334

IEEE

Ayman S. Ghabayen and Shahrul Azman Mohd Noah,"Using Tags for Measuring the Semantic Similarity of Users to Enhance Collaborative Filtering Recommender Systems," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 6, pp. 2063-2070, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.6.1826.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Ghabayen, Ayman S.
AU  - Noah, Shahrul Azman Mohd
PY  - 2017
TI  - Using Tags for Measuring the Semantic Similarity of Users to Enhance Collaborative Filtering Recommender Systems
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 6
Y2  - 2017
SP  - 2063
EP  - 2070
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - recommendation system; collaborative filtering; social tagging system
N2  - Recent years have seen a significant growth in social tagging systems, which allow users to use their own generated tags to organize, categorize, describe and search digital content on social media. The growing popularity of tagging systems is leading to an increasing need for automatic generation of recommended items for users. Much previous research focuses on incorporating recommender techniques in social tagging systems to support the suggestion of suitable tags for annotating related items. Collaborative filtering is one such technique. The most critical task in collaborative filtering is finding related users with similar preferences, i.e., “liked-minded” users. Despite the popularity of collaborative filtering, it still suffers from certain limitations in relation to “cold-start” users, for example, where often there are insufficient preferences to make recommendations. Moreover, there is the data-sparsity problem, where there is limited user feedback data to identify similarities in users’ interests because there is no intersection between users’ transactional data a situation which also results in degraded recommendation quality. For this reason, in this paper we present a new collaborative filtering approach based on users’ semantic tags, which calculates the similarity between users by discovering the semantic spaces in their posted tags. We believe that this approach better reflects the semantic similarity between users according to their tagging perspectives and consequently improves recommendations through the identification of semantically related items for each user. Our experiment on a real-life dataset shows that the proposed approach outperforms the traditional user-based collaborative filtering approach in terms of improving the quality of recommendations.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1826
DO  - 10.18517/ijaseit.7.6.1826

RefWorks

RT Journal Article
ID 1826
A1 Ghabayen, Ayman S.
A1 Noah, Shahrul Azman Mohd
T1 Using Tags for Measuring the Semantic Similarity of Users to Enhance Collaborative Filtering Recommender Systems
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 6
YR 2017
SP 2063
OP 2070
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 recommendation system; collaborative filtering; social tagging system
AB Recent years have seen a significant growth in social tagging systems, which allow users to use their own generated tags to organize, categorize, describe and search digital content on social media. The growing popularity of tagging systems is leading to an increasing need for automatic generation of recommended items for users. Much previous research focuses on incorporating recommender techniques in social tagging systems to support the suggestion of suitable tags for annotating related items. Collaborative filtering is one such technique. The most critical task in collaborative filtering is finding related users with similar preferences, i.e., “liked-minded” users. Despite the popularity of collaborative filtering, it still suffers from certain limitations in relation to “cold-start” users, for example, where often there are insufficient preferences to make recommendations. Moreover, there is the data-sparsity problem, where there is limited user feedback data to identify similarities in users’ interests because there is no intersection between users’ transactional data a situation which also results in degraded recommendation quality. For this reason, in this paper we present a new collaborative filtering approach based on users’ semantic tags, which calculates the similarity between users by discovering the semantic spaces in their posted tags. We believe that this approach better reflects the semantic similarity between users according to their tagging perspectives and consequently improves recommendations through the identification of semantically related items for each user. Our experiment on a real-life dataset shows that the proposed approach outperforms the traditional user-based collaborative filtering approach in terms of improving the quality of recommendations.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1826
DO  - 10.18517/ijaseit.7.6.1826