GRU4Rec: Session-Based Recommendations with Recurrent Neural Networks (2015)
- Paper: Session-based Recommendations with Recurrent Neural Networks (arXiv:1511.06939)
- Authors: Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk
- Venue: ICLR 2016; originally posted to arXiv in November 2015
- Category: Sequential / Session-based Recommendation
- BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations
- EAGER: Two-Stream Generative Recommender
- Efficient On-Device Session-Based Recommendation (ACM TORS)
- How to Index Item IDs for Recommendation Foundation Models (P5, SIGIR)
- LLMCDSR: Cross-Domain Sequential Recommendation with LLMs
- Mamba4Rec: Efficient Sequential Recommendation with Selective State Space Models
- Hidasi, B., Karatzoglou, A., Baltrunas, L., & Tikk, D. (2015). Session-based Recommendations with Recurrent Neural Networks. arXiv:1511.06939. https://arxiv.org/abs/1511.06939
Overview
This paper introduces GRU4Rec, the first application of recurrent neural networks to session-based recommendation. In this setting, the system only observes the sequence of clicks within the current (anonymous) session, without access to a long-term user profile, which makes classical collaborative filtering approaches inapplicable.
Key Contributions
1. RNN-based session modeling: A gated recurrent unit (GRU) processes the sequence of clicked items in a session and predicts the next item(s) from the full item catalog, conditioned on the session prefix seen so far. 2. Session-parallel mini-batch training: Instead of processing sessions one by one, independent sessions are processed in parallel within each mini-batch, dramatically improving training efficiency on large datasets. 3. Ranking losses for recommendation: Since scoring the full item space at every step is infeasible, the paper proposes point-wise and pair-wise ranking losses adapted for the task — notably BPR-max and TOP1 — combined with negative sampling over a large item set. 4. Strong empirical gains: On the RecSys Challenge 2015 e-commerce dataset and a video streaming dataset, GRU4Rec substantially outperformed strong session-based baselines, including item-to-item k-nearest-neighbor recommenders and Markov-chain methods.
Impact
GRU4Rec established neural sequence models as the dominant paradigm for session-based and sequential recommendation. It directly inspired follow-up work such as BERT4Rec, Mamba4Rec, and many transformer-based generative recommenders (e.g., P5), and remains a standard baseline in sequential recommendation research.