# Coarse-to-Fine Sparse Sequential Recommendation

arXiv: https://arxiv.org/abs/2204.01839 · HTML: https://mlsanity.com/paper/2204.01839/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 4 Apr 2022.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017)
- [Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation](https://mlsanity.com/paper/1609.09152/) (2016)
- [A Simple Convolutional Generative Network for Next Item Recommendation](https://mlsanity.com/paper/1808.05163/) (2018)
- [Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation](https://mlsanity.com/paper/2103.03578/) (2021)
- [Improving End-to-End Sequential Recommendations with Intent-aware Diversification](https://mlsanity.com/paper/1908.10171/) (2019)

## Built on by (most cited first, 8 of 8)

- [Personalized Behavior-Aware Transformer for Multi-Behavior Sequential Recommendation](https://mlsanity.com/paper/2402.14473/) (2024)
- [Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation](https://mlsanity.com/paper/2501.17670/) (2025)
- [CoPL: Collaborative Preference Learning for Personalizing LLMs](https://mlsanity.com/paper/2503.01658/) (2025)
- [Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation](https://mlsanity.com/paper/2410.22790/) (2024)
- [FLARE: Fusing Language Models and Collaborative Architectures for Recommender Enhancement](https://mlsanity.com/paper/2409.11699/) (2024)
- [Improving Sequential Recommendation Models with an Enhanced Loss Function](https://mlsanity.com/paper/2301.00979/) (2023)
- [Learning to Personalize Recommendation based on Customers' Shopping Intents](https://mlsanity.com/paper/2305.05279/) (2023)
- [G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer](https://mlsanity.com/paper/2306.14314/) (2023)

## Abstract (arXiv)

Sequential recommendation aims to model dynamic user behavior from historical interactions. Self-attentive methods have proven effective at capturing short-term dynamics and long-term preferences. Despite their success, these approaches still struggle to model sparse data, on which they struggle to learn high-quality item representations. We propose to model user dynamics from shopping intents and interacted items simultaneously. The learned intents are coarse-grained and work as prior knowledge for item recommendation. To this end, we present a coarse-to-fine self-attention framework, namely CaFe, which explicitly learns coarse-grained and fine-grained sequential dynamics. Specifically, CaFe first learns intents from coarse-grained sequences which are dense and hence provide high-quality user intent representations. Then, CaFe fuses intent representations into item encoder outputs to obtain improved item representations. Finally, we infer recommended items based on representations of items and corresponding intents. Experiments on sparse datasets show that CaFe outperforms state-of-the-art self-attentive recommenders by 44.03% NDCG@5 on average.