A pipelined hybrid recommender system for ranking the items on the display

In a session-based recommendation service, currently offered by many online companies including trivago, it is important to effectively incorporate user interactions into recommendations. However, a major challenge lies in the fact that both inter-session and intra-session contexts should be considered at the same time for recommendations to become effective. To address this issue, we propose a pipelined hybrid recommender system that considers the two contexts simultaneously via weighted summation of loss functions designed for the combination of a recurrent neural network (RNN) and a convolutional neural network (CNN). With the hybrid system, our team, OSI LAB, achieved the final score of 0.670167 and reached the 16th place in the RecSys Challenge 2019. Our source code is available from https://github.com/jhoon-oh/recsys2019challenge.