Simple and Efficient Recommendation Strategy for Warm/Cold Sessions for RecSys Challenge 2022

Abstract

In this paper, we describe our approach for the RecSys 2022 Challenge organized by Dressipi. The goal of the challenge is to predict which item is purchased next given sessions of users as well as metadata of items from fashion e-commerce service. One key characteristic of this problem is that most sessions only have few (lower than 3) previous views. Furthermore, a large number of sessions (about 19%) contain views and purchases of items that did not appear before. We propose the following approaches to overcome these problems. First, we introduce a simple, yet strong sequence-aware MLP that outperforms recently proposed sequential recommenders such as BERT4Rec and GRU4Rec in the given dataset. Secondly, we propose a similarity metric that captures not only item metadata but also item popularity. Lastly, we predict recommendations for different types of sessions with different serving models.