Session-based item recommendation with pairwise features

Abstract

The RecSys Challenge 2019 seeks a better solution for item recommendation on short session-based data with limited user history. This paper describes the team PVZ's approach to this challenge, which won the 3rd place in the contest. Our solution consists of the following components. Firstly, we cast the hotel recommendation task as a binary classification problem. Secondly, we spend most of the time doing feature engineering and mining a series of useful features in various aspects. Then we train individual models with a different set of features and blend them with some important features using stacking method. At last, we create other new pair-wise features based on the existing model predictions and train a stacking model again which generates our final result. Our final solution achieved a public score of 0.685929 and a private score of 0.684071, ranking the third place on both sides.