Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based Recommendation

ACM Conference on Recommender Systems (RecSys)


Much of the recent progress in sequential and session-based recommendation has been driven by improvements in model architecture and pretraining techniques originating in the field of Natural Language Processing. Transformer architectures in particular have facilitated building higher-capacity models and provided data augmentation and training techniques which demonstrably improve the effectiveness of sequential recommendation. But with a thousandfold more research going on in NLP, the application of transformers for recommendation understandably lags behind. To remedy this we introduce Transformers4Rec, an open-source library built upon HuggingFace’s Transformers library with a similar goal of opening up the advances of NLP based Transformers to the recommender system community and making these advancements immediately accessible for the tasks of sequential and session-based recommendation. Like its core dependency, Transformers4Rec is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments.

In order to demonstrate the usefulness of the library and the applicability of Transformer architectures in next-click prediction for user sessions, where sequence lengths are much shorter than those commonly found in NLP, we have leveraged Transformers4Rec to win two recent session-based recommendation competitions. In addition, we present in this paper the first comprehensive empirical analysis comparing many Transformer architectures and training approaches for the task of session-based recommendation. We demonstrate that the best Transformer architectures have superior performance across two e-commerce datasets while performing similarly to the baselines on two news datasets. We further evaluate in isolation the effectiveness of the different training techniques used in causal language modeling, masked language modeling, permutation language modeling and replacement token detection for a single Transformer architecture, XLNet. We establish that training XLNet with replacement token detection performs well across all datasets. Finally, we explore techniques to include side information such as item and user context features in order to establish best practices and show that the inclusion of side information uniformly improves recommendation performance.

Related Publications

All Publications

NeurIPS - December 5, 2021

Interpretable agent communication from scratch (with a generic visual processor emerging on the side)

Roberto Dessì, Eugene Kharitonov, Marco Baroni

Workshop on Online Abuse and Harms (WHOAH) at ACL - November 30, 2021

Findings of the WOAH 5 Shared Task on Fine Grained Hateful Memes Detection

Lambert Mathias, Shaoliang Nie, Bertie Vidgen, Aida Davani, Zeerak Waseem, Douwe Kiela, Vinodkumar Prabhakaran

Journal of Big Data - November 6, 2021

A graphical method of cumulative differences between two subpopulations

Mark Tygert

NeurIPS - December 6, 2021

Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement

Samuel Daulton, Maximilian Balandat, Eytan Bakshy

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookie Policy