# Hierarchical Context enabled Recurrent Neural Network for Recommendation

arXiv: https://arxiv.org/abs/1904.12674 · HTML: https://mlsanity.com/paper/1904.12674/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation](https://mlsanity.com/paper/2110.03996/) (2021)
- [HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records](https://mlsanity.com/paper/2205.11680/) (2022)

## Abstract (arXiv)

A long user history inevitably reflects the transitions of personal interests over time. The analyses on the user history require the robust sequential model to anticipate the transitions and the decays of user interests. The user history is often modeled by various RNN structures, but the RNN structures in the recommendation system still suffer from the long-term dependency and the interest drifts. To resolve these challenges, we suggest HCRNN with three hierarchical contexts of the global, the local, and the temporary interests. This structure is designed to withhold the global long-term interest of users, to reflect the local sub-sequence interests, and to attend the temporary interests of each transition. Besides, we propose a hierarchical context-based gate structure to incorporate our textitinterest drift assumption. As we suggest a new RNN structure, we support HCRNN with a complementary textitbi-channel attention structure to utilize hierarchical context. We experimented the suggested structure on the sequential recommendation tasks with CiteULike, MovieLens, and LastFM, and our model showed the best performances in the sequential recommendations.