# Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1706.07684 · HTML: https://mlsanity.com/paper/1706.07684/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2017.

## Built on by (most cited first, 11 of 11)

- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence](https://mlsanity.com/paper/2106.08710/) (2021)
- [Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce](https://mlsanity.com/paper/2110.11072/) (2021)
- [Bidirectional Knowledge Distillation for Enhancing Sequential Recommendation with Large Language Models](https://mlsanity.com/paper/2505.18120/) (2025)
- [Dynamic Customer Embeddings for Financial Service Applications](https://mlsanity.com/paper/2106.11880/) (2021)
- [Metric Learning for Session-based Recommendations](https://mlsanity.com/paper/2101.02655/) (2021)
- [Learning Complex Users' Preferences for Recommender Systems](https://mlsanity.com/paper/2107.01529/) (2021)
- [Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels](https://mlsanity.com/paper/2102.06950/) (2021)
- [Fine-Grained Session Recommendations in E-commerce using Deep Reinforcement Learning](https://mlsanity.com/paper/2210.15451/) (2022)
- [Explicit Context Integrated Recurrent Neural Network for Sensor Data Applications](https://mlsanity.com/paper/2301.05031/) (2023)
- [Enriching User Shopping History: Empowering E-commerce with a Hierarchical Recommendation System](https://mlsanity.com/paper/2403.12096/) (2024)

## Abstract (arXiv)

Recommendations can greatly benefit from good representations of the user state at recommendation time. Recent approaches that leverage Recurrent Neural Networks (RNNs) for session-based recommendations have shown that Deep Learning models can provide useful user representations for recommendation. However, current RNN modeling approaches summarize the user state by only taking into account the sequence of items that the user has interacted with in the past, without taking into account other essential types of context information such as the associated types of user-item interactions, the time gaps between events and the time of day for each interaction. To address this, we propose a new class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that can take into account the contextual information both in the input and output layers and modifying the behavior of the RNN by combining the context embedding with the item embedding and more explicitly, in the model dynamics, by parametrizing the hidden unit transitions as a function of context information. We compare our CRNNs approach with RNNs and non-sequential baselines and show good improvements on the next event prediction task.