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A Fuzzy Logic Based Personalized Recommender System

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The ever-increasing number of E-commerce sites on the Internet has brought about information overload. This has made it difficult for consumers of certain products to find information about such products in an attempt to purchase products that best satisfies them. It has equally reduced the volume of product sales in the E-commerce domain. Hence, this paper proposes a personalized recommender system driven by fuzzy logic technique. The proposed system intelligently mines information about the features of laptop computers and provides professional services to potential buyers by recommending optimal products based on their personal needs. Fuzzy Near Compactness (FNC) concept is employed to measure the similarity between consumer needs and product features in order to recommend optimal products to potential buyers. Experimental result of the proposed system with 50 laptop computers consisting of Acer, Dell, HP, Sony, and Toshiba proves its effectiveness.

The incessant growth of the Web has led to rapid expansion of e-commerce among other things. The large amount of product information on the Web poses great challenges to both customers and online businesses. More customers are turning towards online shopping because it is relatively convenient, reliable, and fast; yet such customers usually experience difficulty in searching for products on the Web due to information overload. Online businesses have often been overwhelmed by the rich data they have collected and find it difficult to promote products appropriate to specific customers. There is also the problem of ineffective utilization of the available large amount of product information from online transactions to support better decision making by both buyers and sellers [1]. To address these information overload problems, e-commerce stores are now applying mass customization principles not to the products but to their presentation in the on-line store [24]. One way to achieve mass customization in e-commerce is the use of recommender systems.

Recommender systems are used by an ever-increasing number of E-commerce sites to help consumers find products that best suit their needs [3]. Typically, a recommender system analyzes data about items, or interactions between users and items in order to find associations between items and users. It provides advice to users about items they might wish to purchase or examine. The recommendations made by such a system can help users navigate through large information spaces of product descriptions, news articles or other items [1][2]. Various factors are considered when recommending products to online buyers; these include: top sellers of a particular product, demographic information of buyers, and analysis of the past buying behavior of customers to predict their buying behaviors in the future. These forms of recommendation include suggesting products to the consumer, providing personalized product information, summarizing community opinions, and providing community critiques.

by: Ojokoh, B. A., Omisore, M. O, Samuel, O. W, and Ogunniyi, T. O

File Information :English Language/8 Page/Size :315 K

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کاری از: Ojokoh, B. A., Omisore, M. O, Samuel, O. W, and Ogunniyi, T. O

اطلاعات فایل :زبان انگلیسی /8صفحه /حجم :315 K

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