توصیه شخصی شده فروشنده قابل اعتماد در بازار آزاد

Although more and more customers are buying products on online stores, they have a difficulty in selecting a both trustworthy and suitable seller who sells a product they want to buy since there is a plenty number of sellers who sell the same product with different options. Therefore, the objective of this research is to propose a personalized trustworthy seller recommendation system for the customers of an open market in Korea. To that end, we first developed a module which classifies sellers into trustworthy one or not using a classification technique such as decision tree, and then developed another module which makes use of the content-based filtering method to find best-matching top k sellers among the selected trustworthy sellers. Experimental results show that our approach is worthwhile to take. This study makes a contribution at least in that to our knowledge it is the first attempt to recommend sellers, not products as done in most other studies, to customers.

The wide spread of Internet infrastructure facilitated e-transactions all around the world since early 1990s. Especially in Korea, this type of phenomenon has been observed since then. According to OECD reports, the percentage of the households which have access to the internet is 80%, the deployment ratio of DSL (Digital Subscriber Line) is about 99.5%, and that of broadband cable is approximately 57%, in Korea. Consequently, such high coverage of infrastructure stimulated e-commerce transactions more than in other countries. Behind the ever-expanded e-commerce industry, there have been new types of problem. That is, relatively low cost of starting a new business in e-commerce would cause more fierce competition among sellers, while providing more choices to the customers (Choi, Stahl, & Whinston, 1997). Therefore, the participants of online transactions can commonly face with significant problems such as “trust” (Hoffman et al., 1999 and Chen and Barnes, 2007; McKnight & Chervany, 2001) and “information overload” (Mooney & Roy, 2000; Yu, Schwaighofer, Tresp, Xu, & Kriegel, 2004).

When customers try to buy a product in an open market, most of them often hesitate to purchase it mainly because they may do not have trust in sellers in the open market. If the open market recommends several trustworthy sellers for a target customer who wants to buy a product, it will not be the case. Therefore, we proposed a content-based seller recommendation system, which first classifies (thus identifies) trustworthy sellers using the decision tree algorithm, and then recommends top k sellers among the trustworthy sellers to the target customer, employing a CBF approach based on profiles of sellers and the target customer. The results of our experiments show our approach to recommending trustworthy seller is satisfactory and worthwhile, and therefore we expect that our seller recommendation system will bring benefits both to customers and to the open market.