期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2013
卷号：3
期号：6
页码：751-761
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：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.DOI:http://dx.doi.org/10.11591/ijece.v3i6.3931
其他摘要：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. DOI: http://dx.doi.org/10.11591/ijece.v3i6.3931
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