Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts

Abstract

In our article Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts accepted for publication in User Modeling and User-Adapted Interaction (UMUAI) we examined a classification-based approach to analyze what makes a recommendation successful. In the process we generated over 95 features for each single recommendation action in our data set provided by the online fashion retailer Zalando. Due to space issues we could only explain some of the most relevant features in the article itself. As an addition, the following table lists all investigated features in detail. Furthermore, in our article we reported the top ten feature weights regarding the label prediction calculated by the methods Gain ratio and Chi-squared to highlight the most important success signals. Here, we additionally reveal the weights for all features and also include the Information gain ratio and the Gini index.