Recommending Long-Tail Items Using Extended Tripartite Graphs

Abstract

With the popular and increasing power of the Internet these days, the effort of distributing and inventory costs of stocking various online retailing items are nearly negligible. In addition to selling popular, called "short-head", items in large quantities, online retailers, such as Amazon, offer a large number of unique items, called "long tail", with relatively small quantities sold. Retailers realize that it has high value to sell items from the long-tail category, since for users these long-tail items could meet the interest of them and surprise them simultaneously. Retailers also recognize that long-tail items can be an untapped source of revenue for a business; however, it is difficult to connect customers with long-tail items they are interested in, since they are unaware of them. Recommender systems help bridge the gap between users and long-tail items by learning user preferences and recommending appropriate items to them. In this paper, we propose a new tripartite graph recommender system, which is designed to suggest long-tail items. Compared with other graph-based recommender systems, our proposed recommendation system solves the tripartite variant problem suffered by existing approaches for having a low diversity score. A rework of the tripartite graph system is introduced, called the extended tripartite graph system, which enhances the performance of existing long-tail recommendation approaches measured by using two widely-used performance metrics: recall and diversity. Experimental results on the extended tripartite graph algorithm verify its merits and novelty.