# Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1706.04148 · HTML: https://mlsanity.com/paper/1706.04148/

## Key facts

- Cited by 84 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2017.

## Built on by (most cited first, 30 of 84)

- [PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts](https://mlsanity.com/paper/2306.04528/) (2023)
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- [Contrastive Self-supervised Sequential Recommendation with Robust Augmentation](https://mlsanity.com/paper/2108.06479/) (2021)
- [Zero-Shot Recommender Systems](https://mlsanity.com/paper/2105.08318/) (2021)
- [Sequential Recommendation via Stochastic Self-Attention](https://mlsanity.com/paper/2201.06035/) (2022)
- [Decoupled Side Information Fusion for Sequential Recommendation](https://mlsanity.com/paper/2204.11046/) (2022)
- [A Survey on User Behavior Modeling in Recommender Systems](https://mlsanity.com/paper/2302.11087/) (2023)
- [Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation](https://mlsanity.com/paper/2107.03813/) (2021)
- [Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta](https://mlsanity.com/paper/2311.09544/) (2023)
- [Multi-Behavior Sequential Recommendation with Temporal Graph Transformer](https://mlsanity.com/paper/2206.02687/) (2022)
- [Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation](https://mlsanity.com/paper/2110.03996/) (2021)
- [Rethinking Collaborative Metric Learning: Toward an Efficient Alternative without Negative Sampling](https://mlsanity.com/paper/2206.11549/) (2022)
- [One4all User Representation for Recommender Systems in E-commerce](https://mlsanity.com/paper/2106.00573/) (2021)
- [CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendation](https://mlsanity.com/paper/2511.22707/) (2025)
- [The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation](https://mlsanity.com/paper/2202.13307/) (2022)
- [Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search](https://mlsanity.com/paper/2203.15542/) (2022)
- [Improving Sequential Recommendation Consistency with Self-Supervised Imitation](https://mlsanity.com/paper/2106.14031/) (2021)
- [Unsupervised Proxy Selection for Session-based Recommender Systems](https://mlsanity.com/paper/2107.03564/) (2021)
- [Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations](https://mlsanity.com/paper/2107.05235/) (2021)
- [Sequential Recommendation with Diffusion Models](https://mlsanity.com/paper/2304.04541/) (2023)
- [DraftRec: Personalized Draft Recommendation for Winning in Multi-Player Online Battle Arena Games](https://mlsanity.com/paper/2204.12750/) (2022)
- [Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2206.08088/) (2022)
- [StratMed: Relevance Stratification between Biomedical Entities for Sparsity on Medication Recommendation](https://mlsanity.com/paper/2308.16781/) (2023)
- [FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning](https://mlsanity.com/paper/2309.08420/) (2023)
- [Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation](https://mlsanity.com/paper/2109.11790/) (2021)
- [MC2-SF: Slow-Fast Learning for Mobile-Cloud Collaborative Recommendation](https://mlsanity.com/paper/2109.12314/) (2021)
- [Improving Contrastive Learning with Model Augmentation](https://mlsanity.com/paper/2203.15508/) (2022)
- [TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations](https://mlsanity.com/paper/2111.07378/) (2021)
- [Context Uncertainty in Contextual Bandits with Applications to Recommender Systems](https://mlsanity.com/paper/2202.00805/) (2022)

## Abstract (arXiv)

Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings. Recently, Recurrent Neural Networks have been shown to perform very well in session-based settings. While in many session-based recommendation domains user identifiers are hard to come by, there are also domains in which user profiles are readily available. We propose a seamless way to personalize RNN models with cross-session information transfer and devise a Hierarchical RNN model that relays end evolves latent hidden states of the RNNs across user sessions. Results on two industry datasets show large improvements over the session-only RNNs.