A Topic-Based Recommender System for Electronic Marketplace Platforms

A large number of items are placed, 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 propose a topic-based recommender system that exploits the latent semantics in the item descriptions in order to support the activities of buyers and sellers in auction electronic marketplaces. We present the design of our system and demonstrate how it can be used in real life scenarios.