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Recommender system based on semantic similarity

In electronic commerce, in order to help users to find their favourite products, we essentially need a system to classify the products based on the user's interests and needs to recommend them to the users. For the same reason the recommendation systems are designed to help finding information...

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Main Authors: Karamollah, Bagheri Fard, Nilashi, Mehrbakhsh, Rahmani, Mohsen, Ibrahim, Othman
Format: Article
Published: Institute of Advanced Engineering and Science 2013
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/41052/
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id my.utm.41052
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spelling my.utm.410522017-02-15T06:52:54Z http://eprints.utm.my/id/eprint/41052/ Recommender system based on semantic similarity Karamollah, Bagheri Fard Nilashi, Mehrbakhsh Rahmani, Mohsen Ibrahim, Othman QA75 Electronic computers. Computer science In electronic commerce, in order to help users to find their favourite products, we essentially need a system to classify the products based on the user's interests and needs to recommend them to the users. For the same reason the recommendation systems are designed to help finding information in large websites. They are basically developed to offer products to the customers in an automated fashion to help them to do conveniently their shopping. The developing of such systems is important since there are often a large number of factors involved in purchasing a product that would make it difficult for the customer to make the best decision. Finding relationship among users and relationships among products are important issue in these systems. One of relations is similarity. Measure similarity among users and products is used in the pure methods for calculating similarity degree. In this paper, semantic similarity is used to find a set of k nearest neighbours to the target user, or target item. Thus, because of incorporating semantic similarity in the proposed recommendation system, from the experimental results, the high accuracy was obtained on private building company dataset in comparison with state-of-the-art recommender systems. Institute of Advanced Engineering and Science 2013 Article PeerReviewed Karamollah, Bagheri Fard and Nilashi, Mehrbakhsh and Rahmani, Mohsen and Ibrahim, Othman (2013) Recommender system based on semantic similarity. International Journal of Electrical and Computer Engineering (IJECE), 3 (6). pp. 751-761. ISSN 2088-8708
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 QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Karamollah, Bagheri Fard
Nilashi, Mehrbakhsh
Rahmani, Mohsen
Ibrahim, Othman
Recommender system based on semantic similarity
description In electronic commerce, in order to help users to find their favourite products, we essentially need a system to classify the products based on the user's interests and needs to recommend them to the users. For the same reason the recommendation systems are designed to help finding information in large websites. They are basically developed to offer products to the customers in an automated fashion to help them to do conveniently their shopping. The developing of such systems is important since there are often a large number of factors involved in purchasing a product that would make it difficult for the customer to make the best decision. Finding relationship among users and relationships among products are important issue in these systems. One of relations is similarity. Measure similarity among users and products is used in the pure methods for calculating similarity degree. In this paper, semantic similarity is used to find a set of k nearest neighbours to the target user, or target item. Thus, because of incorporating semantic similarity in the proposed recommendation system, from the experimental results, the high accuracy was obtained on private building company dataset in comparison with state-of-the-art recommender systems.
format Article
author Karamollah, Bagheri Fard
Nilashi, Mehrbakhsh
Rahmani, Mohsen
Ibrahim, Othman
author_facet Karamollah, Bagheri Fard
Nilashi, Mehrbakhsh
Rahmani, Mohsen
Ibrahim, Othman
author_sort Karamollah, Bagheri Fard
title Recommender system based on semantic similarity
title_short Recommender system based on semantic similarity
title_full Recommender system based on semantic similarity
title_fullStr Recommender system based on semantic similarity
title_full_unstemmed Recommender system based on semantic similarity
title_sort recommender system based on semantic similarity
publisher Institute of Advanced Engineering and Science
publishDate 2013
url http://eprints.utm.my/id/eprint/41052/
_version_ 1643650632320548864
score 13.211869

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