# Improved Recurrent Neural Networks for Session-based Recommendations

arXiv: https://arxiv.org/abs/1606.08117 · HTML: https://mlsanity.com/paper/1606.08117/

## Key facts

- Cited by 86 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2016.

## Built on by (most cited first, 30 of 86)

- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest](https://mlsanity.com/paper/2306.00248/) (2023)
- [AutoLoss: Automated Loss Function Search in Recommendations](https://mlsanity.com/paper/2106.06713/) (2021)
- [Large Language Models for Intent-Driven Session Recommendations](https://mlsanity.com/paper/2312.07552/) (2023)
- [A Survey on User Behavior Modeling in Recommender Systems](https://mlsanity.com/paper/2302.11087/) (2023)
- [A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models](https://mlsanity.com/paper/2107.13045/) (2021)
- [AutoMLP: Automated MLP for Sequential Recommendations](https://mlsanity.com/paper/2303.06337/) (2023)
- [RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems](https://mlsanity.com/paper/2103.08057/) (2021)
- [Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters](https://mlsanity.com/paper/2111.05897/) (2021)
- [Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation](https://mlsanity.com/paper/2205.04181/) (2022)
- [BagPipe: Accelerating Deep Recommendation Model Training](https://mlsanity.com/paper/2202.12429/) (2022)
- [MobileRec: A Large-Scale Dataset for Mobile Apps Recommendation](https://mlsanity.com/paper/2303.06588/) (2023)
- [Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation](https://mlsanity.com/paper/2403.16427/) (2024)
- [Unleash LLMs Potential for Sequential Recommendation by Coordinating Dual Dynamic Index Mechanism](https://mlsanity.com/paper/2409.09253/) (2024)
- [Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce](https://mlsanity.com/paper/2110.11072/) (2021)
- [Scaling Sequential Recommendation Models with Transformers](https://mlsanity.com/paper/2412.07585/) (2024)
- [Molar: Multimodal LLMs with Collaborative Filtering Alignment for Enhanced Sequential Recommendation](https://mlsanity.com/paper/2412.18176/) (2024)
- [Universal Item Tokenization for Transferable Generative Recommendation](https://mlsanity.com/paper/2504.04405/) (2025)
- [English to Bangla Machine Translation Using Recurrent Neural Network](https://mlsanity.com/paper/2106.07225/) (2021)
- [Towards Explainable Temporal User Profiling with LLMs](https://mlsanity.com/paper/2505.00886/) (2025)
- [Data Augmentation Strategies for Improving Sequential Recommender Systems](https://mlsanity.com/paper/2203.14037/) (2022)
- [Pre-training Generative Recommender with Multi-Identifier Item Tokenization](https://mlsanity.com/paper/2504.04400/) (2025)
- [Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search](https://mlsanity.com/paper/2107.07173/) (2021)
- [Augmenting the User-Item Graph with Textual Similarity Models](https://mlsanity.com/paper/2109.09358/) (2021)
- [HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List Continuation](https://mlsanity.com/paper/2110.01467/) (2021)
- [Breaking the Softmax Bottleneck for Sequential Recommender Systems with Dropout and Decoupling](https://mlsanity.com/paper/2110.05409/) (2021)
- [Dynamic Review-based Recommenders](https://mlsanity.com/paper/2110.14747/) (2021)
- [STAR: A Session-Based Time-Aware Recommender System](https://mlsanity.com/paper/2211.06394/) (2022)
- [Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation](https://mlsanity.com/paper/2301.03780/) (2023)

## Abstract (arXiv)

Recurrent neural networks (RNNs) were recently proposed for the session-based recommendation task. The models showed promising improvements over traditional recommendation approaches. In this work, we further study RNN-based models for session-based recommendations. We propose the application of two techniques to improve model performance, namely, data augmentation, and a method to account for shifts in the input data distribution. We also empirically study the use of generalised distillation, and a novel alternative model that directly predicts item embeddings. Experiments on the RecSys Challenge 2015 dataset demonstrate relative improvements of 12.8% and 14.8% over previously reported results on the Recall@20 and Mean Reciprocal Rank@20 metrics respectively.