Soft computing techniques for product filtering in E-commerce personalisation: A comparison study (original) (raw)

Products Recommendation in a B2CE-Commerce Using Fuzzy Logic

International Journal of Computer Theory and Engineering, 2009

Customer is the most important success factor for Business to Costumer (B2C) e-Commerce. There are two important ways have been used nowadays which are data mining and live customer support. These two ways are effective and reliable, but each one has its own problem. In this paper, an intelligent algorithm developed to replace these two methods with fuzzy rules. The fuzzy rules are generated from history data mining and an expert converts that data to rules. The solutions made through designing and implementing two databases, one for the fuzzy memberships and the other for the e-Commerce catalogue system. Then using PHP programming language, a script made to deal with these databases and link between them, then read data and process them using fuzzy logic to generate a recommendation to the customer. The algorithm is applied to three kinds of products, and the results are compared with Amazon site and give high agreement.

Survey Paper on Fuzzy Logic Based Recommended System

2017

with current projections regarding the growth of Internet sales, online retailing raises many questions about how to market on the Net. A Recommender System (RS) is a composition of software tools that provides valuable piece of advice for items or services chosen by a user. Recommender systems are currently useful in both the research and in the commercial areas. Recommender systems are a means of personalizing a site and a solution to the customer’s information overload problem. Recommender Systems (RS) are software tools and techniques providing suggestions for items and/or services to be of use to a user. These systems are achieving widespread success in ecommerce applications now a days, with the advent of internet. This paper presents a categorical review of the field of recommender systems and describes the state-of-the-art of the recommendation methods that are usually classified into four categories: Content based Collaborative, Demographic and Hybrid systems. To build our ...

A Fuzzy Logic Based Personalized Recommender System

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 mine information about the features of laptop computers and provide 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.