# Next Item Recommendation with Self-Attention

arXiv: https://arxiv.org/abs/1808.06414 · HTML: https://mlsanity.com/paper/1808.06414/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2018.

## Built on by (most cited first, 15 of 15)

- [Recommender Systems with Generative Retrieval](https://mlsanity.com/paper/2305.05065/) (2023)
- [A General Survey on Attention Mechanisms in Deep Learning](https://mlsanity.com/paper/2203.14263/) (2022)
- [Dynamic Memory based Attention Network for Sequential Recommendation](https://mlsanity.com/paper/2102.09269/) (2021)
- [Unsupervised Proxy Selection for Session-based Recommender Systems](https://mlsanity.com/paper/2107.03564/) (2021)
- [Sequential Recommendation Model for Next Purchase Prediction](https://mlsanity.com/paper/2207.06225/) (2022)
- [EulerFormer: Sequential User Behavior Modeling with Complex Vector Attention](https://mlsanity.com/paper/2403.17729/) (2024)
- [DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation](https://mlsanity.com/paper/2406.08804/) (2024)
- [Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction](https://mlsanity.com/paper/2303.04744/) (2023)
- [Search Intenion Network for Personalized Query Auto-Completion in E-Commerce](https://mlsanity.com/paper/2403.02609/) (2024)
- [ELASTIC: Efficient Linear Attention for Sequential Interest Compression](https://mlsanity.com/paper/2408.09380/) (2024)
- [Robust Recommendation with Implicit Feedback via Eliminating the Effects of Unexpected Behaviors](https://mlsanity.com/paper/2112.11023/) (2021)
- [Memory efficient location recommendation through proximity-aware representation](https://mlsanity.com/paper/2310.06484/) (2023)
- [Social Recommendation through Heterogeneous Graph Modeling of the Long-term and Short-term Preference Defined by Dynamic Time Spans](https://mlsanity.com/paper/2312.14306/) (2023)
- [Multi-Item-Query Attention for Stable Sequential Recommendation](https://mlsanity.com/paper/2509.24424/) (2025)
- [Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/2607.19253/) (2026)

## Abstract (arXiv)

In this paper, we propose a novel sequence-aware recommendation model. Our model utilizes self-attention mechanism to infer the item-item relationship from user's historical interactions. With self-attention, it is able to estimate the relative weights of each item in user interaction trajectories to learn better representations for user's transient interests. The model is finally trained in a metric learning framework, taking both short-term and long-term intentions into consideration. Experiments on a wide range of datasets on different domains demonstrate that our approach outperforms the state-of-the-art by a wide margin.