Two-Stage Session-based Recommendations with Candidate Rank Embeddings

TL;DR

This paper introduces a two-stage session-based recommendation approach using candidate rank embeddings to enhance the ranking of relevant items, demonstrating significant improvements in recall, MRR, and click-through rates.

Contribution

It proposes a novel candidate rank embedding method that improves session-based recommendation accuracy by focusing on re-ranking relevant items using session information.

Findings

Significant improvements in Recall and MRR at 20 on Fashion-Similar dataset.

Enhanced next click prediction performance on real-world datasets.

Effective re-ranking strategy with candidate rank embeddings.

Abstract

Recent advances in Session-based recommender systems have gained attention due to their potential of providing real-time personalized recommendations with high recall, especially when compared to traditional methods like matrix factorization and item-based collaborative filtering. Nowadays, two of the most recent methods are Short-Term Attention/Memory Priority Model for Session-based Recommendation (STAMP) and Neural Attentive Session-based Recommendation (NARM). However, when these two methods were applied in the similar-item recommendation dataset of Zalando (Fashion-Similar), they did not work out-of-the-box compared to a simple Collaborative-Filtering approach. Aiming for improving the similar-item recommendation, we propose to concentrate efforts on enhancing the rank of the few most relevant items from the original recommendations, by employing the information of the session of the user encoded by an attention network. The efficacy of this strategy was confirmed when using a novel Candidate Rank Embedding that encodes the global ranking information of each candidate in the re-ranking process. Experimental results in Fashion-Similar show significant improvements over the baseline on Recall and MRR at 20, as well as improvements in Click Through Rate based on an online test. Additionally, it is important to point out from the evaluation that was performed the potential of this method on the next click prediction problem because when applied to STAMP and NARM, it improves the Recall and MRR at 20 on two publicly available real-world datasets.