Predicting Offer Popularity in E-commerce Environment

Increasing popularity of buying goods online causes a rapid growth of a number of e-shops. Many different sellers often offer many similar products which differ only in a few details and thus buyers often make decisions based on less “product-related” and more “offer-related” attributes such as length of description or quality of illustrative photos.

Usually, it takes some time to find out if an offer is considered attractive by potential buyers and meanwhile some competitors could have offered a very similar product in a more attractive way. This means the one who knows what attributes a popular offer should have and when it should be published rules the market.

There are many works on predicting the popularity of items after they are published. In our work, we focus on creating a model to predict a product popularity prior to its publication. Firstly we want to choose a proper classifier and then we want to provide some kind of offer modification recommendation as well.