Abstract—Affiliate Networks are the main source of communication between publishers and advertisers where publishers normally subscribe as a service provider and advertisers as an employer. These networks are helping both the publishers and advertisers in terms of providing them with a platform where they can build an automated affiliate connection with each other via these affiliate networks. The problem that is highlighted in this paper is the huge gap that exists between the publisher and advertiser in these affiliate networks and a solution is provided by proposing a priority recommendation system based on K-Means clustering algorithm. Every advertiser desires to have that type of publisher who is already practiced in his category of business or at least has the same skills and talent. This paper presents the concept of a recommendation system based on clustering the real-time data of all the existing transactions of publishers and advertisers of an affiliate network and based on the resulting POST-HOC classified data, a new publisher or advertiser will automatically be classified. Real-time data is provided by Affiliate Future a well-known company among all the affiliate networks. After carefully examining the data the most effective attribute is selected as the base attribute for clustering. The data is encoded into binary numbers for the purpose of clustering. More than one distance approaches are used and the most suitable one is selected for classifying the data.

Index Terms—affiliate marketing, clustering, publisher, advertiser, sammon mapping  

Cite: Zeeshan Khawar Malik, Colin Fyfe, and Malcolm Crowe, "Priority Recommendation System in an Affiliate Network," Journal of Emerging Technologies in Web Intelligence, Vol. 5, No. 3, pp. 222-229, August 2013. doi:10.4304/jetwi.5.3.222-229