eSASRec: Enhancing Transformer-based Recommendations in a Modular Fashion

TL;DR

eSASRec is a modular enhancement of Transformer-based sequential recommendation models that combines existing improvements to achieve state-of-the-art performance with simple integration.

Contribution

The paper systematically benchmarks modular improvements to Transformer models and introduces eSASRec, a strong, easy-to-implement recommendation model combining effective techniques.

Findings

eSASRec outperforms recent state-of-the-art models by 23% on academic benchmarks.

In production-like settings, eSASRec achieves a favorable accuracy-coverage tradeoff.

eSASRec is simple to integrate and serves as a strong baseline for future models.

Abstract

Since their introduction, Transformer-based models, such as SASRec and BERT4Rec, have become common baselines for sequential recommendations, surpassing earlier neural and non-neural methods. A number of following publications have shown that the effectiveness of these models can be improved by, for example, slightly updating the architecture of the Transformer layers, using better training objectives, and employing improved loss functions. However, the additivity of these modular improvements has not been systematically benchmarked - this is the gap we aim to close in this paper. Through our experiments, we identify a very strong model that uses SASRec's training objective, LiGR Transformer layers, and Sampled Softmax Loss. We call this combination eSASRec (Enhanced SASRec). While we primarily focus on realistic, production-like evaluation, in our preliminarily study we find that common academic benchmarks show eSASRec to be 23% more effective compared to the most recent state-of-the-art models, such as ActionPiece. In our main production-like benchmark, eSASRec resides on the Pareto frontier in terms of the accuracy-coverage tradeoff (alongside the recent industrial models HSTU and FuXi. As the modifications compared to the original SASRec are relatively straightforward and no extra features are needed (such as timestamps in HSTU), we believe that eSASRec can be easily integrated into existing recommendation pipelines and can can serve as a strong yet very simple baseline for emerging complicated algorithms. To facilitate this, we provide the open-source implementations for our models and benchmarks in repository https://github.com/blondered/transformer_benchmark