International Journal of Computer Theory and Engineering, Vol. 1, No. 2, June 2009 1793-8201 - 183 - Abstract — 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. Index Terms —E-commerce service; B2C; Fuzzy logic, Products Recommendation I. INTRODUCTION In the 21st century, e-Commerce might not be able to guarantee making money for the company, but a company, which doesn’t think about the issue of commerce, will definitely face the problem of competence deficiency [1]. One of the most important types of e-Commerce, classified according to the nature of the supplier and client, is Business to Consumer (B2C). In this type that consumer accesses the system of the supplier [2]. Extensive research has been done investigating the role of trust in online purchase intention and behaviors, and the difference between initial and repeat trust formation and maintenance [3]. On the other hand, much research on the area of recommendation systems has focused on improving recommendation accuracy and computational performances. Recommendation systems have achieved great success in generating accurate and personalized recommendations to their customers [4]. The two important ways have been used nowadays which are data mining and live customer support [5]. The motivation of this paper aims is to replace the data mining methods and human live support by an intelligent system that uses the fuzzy logic, that use rule base made by a human expert, to generate a recommendation to the customer. In other words, this project aims to design an expert e-Commerce System (EECS) that helps the customer who browses a site to make the right decision, which means reducing the cost and time for the e-Commerce sites' owners. II. SOLUTION STEPS In this work, an Expert E-Commerce System (EECS) was designed, that helps the customer who browses a site to make the right decision. The proposed system operation depends on fuzzy rules generated by a human experience in an e-Commerce support. So it will work just like data mining method and human resources customer support together, which means reducing the cost and time for the e-Commerce sites' owners. Solution steps to achieve this goal can be summarized as follows: - Development of a general fuzzy variable maker that takes the values from a web form and saves it into a database. - Design and implementation of an e-Commerce catalogue using PHP programming language and MySQL database server. - Making shopping cart script that deals with guests and users, by using sessions' technique. - Integrating the stored fuzzy variable into the general fuzzy maker system. - Finally, applying the proposed system to real data collected from Amazon.com. III. PROPOSED EECS MODEL Fuzzy logic is the core of the EECS, besides database (DB) to read and write data. First a fuzzy variable maker system is made to be a general system that uses standard membership functions S, PI and Z values [6]. Then, these values are used by a fuzzy function programmed to integrate the e-Commerce site with fuzzy. Figure (1) shows the main components of the proposed system and relations between them. Admin area block represents the input for the catalogue, by adding categories and products to the catalogue database (DB). Product catalogue block and shopping cart are related in some table fields in the DB. The combination rounded by the dotted rectangle represents the standard e-Commerce system. On the other side of the block diagram there is the fuzzy component of the EECS system. The fuzzy variable maker is the input of the fuzzy DB and the rule base is flat files. Products Recommendation in a B2C E-Commerce Using Fuzzy Logic Salam A. Ismaeel, Karim M. Ahjebory and Oras I. Sulaiman