文章基本信息

标题：A NOVEL PRODUCT FILTERING AND PRODUCT RECOMMENDATION SYSTEM TO OPTIMIZE THE SEARCH SPACE AND SPARSITY IN REAL TIME ECOMMERCE ENVIRONMENT
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作者：MR.T.SUBHA MASTAN RAO ; DR.G.RAMA KRISHNA
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2015
卷号：81
期号：2
出版社：Journal of Theoretical and Applied
摘要：Online Product recommended system is the most effective prediction system in the e-commerce websites. Customized/Automated recommendation systems can assist the users to find relevant products within short time in large e-commerce databases. Several recommendation techniques have been proposed to filter the user interested products and to optimize e-commerce sales from different vendors. With the tremendous increase in the products and customers in e-commerce systems, the time taken to search the required product using traditional recommended techniques increases due to the large number of products features and its specifications. With the tremendous growth in the products and customers information however, the traditional systems encounter two key issues, enhanced response time and minimized recommending quality. New Qualitative recommended systems are essential to handle. The traditional techniques can�t implement search space optimization when the item-space changes; on the other hand, the overall efficiency of the recommendation system will decrease as the items grow into a large amount. Sparsity of the product is the most significant basis which minimizes the inadequate quality. In this paper, a new product filtering and product recommended system is proposed to optimize the search space and sparsity. Product filtering can be used to filter the relevant products from the web using the conditional probability and similarity values. Product prediction based recommended system is proposed to predict the products ranking based on the products features as well as calculated predicted probabilities. Experimental results show that proposed filtering based recommended system outperforms well on web applications like e-bay in terms of search space and ranking are concern.
关键词：Online; Product Filtering; Qualitative; Sparsity; Product Filtering