# Context-aware Sequential Recommendation

arXiv: https://arxiv.org/abs/1609.05787 · HTML: https://mlsanity.com/paper/1609.05787/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2016.

## Built on by (most cited first, 25 of 25)

- [STAN: Spatio-Temporal Attention Network for Next Location Recommendation](https://mlsanity.com/paper/2102.04095/) (2021)
- [Filter-enhanced MLP is All You Need for Sequential Recommendation](https://mlsanity.com/paper/2202.13556/) (2022)
- [Dynamic Graph Collaborative Filtering](https://mlsanity.com/paper/2101.02844/) (2021)
- [A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems](https://mlsanity.com/paper/2204.06923/) (2022)
- [Decoupled Side Information Fusion for Sequential Recommendation](https://mlsanity.com/paper/2204.11046/) (2022)
- [Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation](https://mlsanity.com/paper/2212.08262/) (2022)
- [Text Matching Improves Sequential Recommendation by Reducing Popularity Biases](https://mlsanity.com/paper/2308.14029/) (2023)
- [Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge](https://mlsanity.com/paper/2208.06089/) (2022)
- [Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations](https://mlsanity.com/paper/2107.05235/) (2021)
- [Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation](https://mlsanity.com/paper/2111.12262/) (2021)
- [Modeling User Viewing Flow Using Large Language Models for Article Recommendation](https://mlsanity.com/paper/2311.07619/) (2023)
- [Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies](https://mlsanity.com/paper/2409.17436/) (2024)
- [Deep Stable Multi-Interest Learning for Out-of-distribution Sequential Recommendation](https://mlsanity.com/paper/2304.05615/) (2023)
- [Efficient and Effective Query Context-Aware Learning-to-Rank Model for Sequential Recommendation](https://mlsanity.com/paper/2507.03789/) (2025)
- [Learning Complex Users' Preferences for Recommender Systems](https://mlsanity.com/paper/2107.01529/) (2021)
- [Transition-Aware Multi-Activity Knowledge Tracing](https://mlsanity.com/paper/2301.12916/) (2023)
- [USE: Dynamic User Modeling with Stateful Sequence Models](https://mlsanity.com/paper/2403.13344/) (2024)
- [TransTARec: Time-Adaptive Translating Embedding Model for Next POI Recommendation](https://mlsanity.com/paper/2404.07096/) (2024)
- [Cross-domain User Preference Learning for Cold-start Recommendation](https://mlsanity.com/paper/2112.03667/) (2021)
- [Automation of Smart Homes with Multiple Rule Sources](https://mlsanity.com/paper/2401.02451/) (2024)
- [Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item Prediction](https://mlsanity.com/paper/2408.15953/) (2024)
- [Multi-intent Aware Contrastive Learning for Sequential Recommendation](https://mlsanity.com/paper/2409.08733/) (2024)
- [Timing Matters: Enhancing User Experience through Temporal Prediction in Smart Homes](https://mlsanity.com/paper/2411.18719/) (2024)
- [Semantic-aware Graph-guided Behavior Sequences Generation with Large Language Models for Smart Homes](https://mlsanity.com/paper/2508.03484/) (2025)
- [Time Matters: A Novel Real-Time Long- and Short-term User Interest Model for Click-Through Rate Prediction](https://mlsanity.com/paper/2511.06213/) (2025)

## Abstract (arXiv)

Since sequential information plays an important role in modeling user behaviors, various sequential recommendation methods have been proposed. Methods based on Markov assumption are widely-used, but independently combine several most recent components. Recently, Recurrent Neural Networks (RNN) based methods have been successfully applied in several sequential modeling tasks. However, for real-world applications, these methods have difficulty in modeling the contextual information, which has been proved to be very important for behavior modeling. In this paper, we propose a novel model, named Context-Aware Recurrent Neural Networks (CA-RNN). Instead of using the constant input matrix and transition matrix in conventional RNN models, CA-RNN employs adaptive context-specific input matrices and adaptive context-specific transition matrices. The adaptive context-specific input matrices capture external situations where user behaviors happen, such as time, location, weather and so on. And the adaptive context-specific transition matrices capture how lengths of time intervals between adjacent behaviors in historical sequences affect the transition of global sequential features. Experimental results show that the proposed CA-RNN model yields significant improvements over state-of-the-art sequential recommendation methods and context-aware recommendation methods on two public datasets, i.e., the Taobao dataset and the Movielens-1M dataset.