A topic-based recommender system for electronic marketplace platforms

A large number of items are described, and subsequently bought and sold every day in auction marketplaces across the web. The amount of information and the number of available items makes finding what to buy as well as describing an item to sell, a challenge for the participants. In this paper we consider two functions of electronic marketplaces. First, we address the recommendation of related items for users browsing the items offered in a marketplace. Second, in order to support potential sellers we propose the recommendation of relevant items and terms which can be used to describe an item to be sold in the marketplace. The contribution of this paper lies in the proposal of an innovative system that exploits the hidden topics of unstructured information found in the e-marketplace in order to support these functions. We propose a three-step process in which a probabilistic topic modelling approach is used in order to uncover latent topics that provide the basis for item and term similarity calculation for the corresponding recommendations. We present the design of our system and perform evaluations of the quality of the extracted topics as well as of the recommender system using real life scenarios using data extracted from a widely used auction marketplace. The evaluations demonstrate the perceived usefulness of our approach.