# Denoising Self-attentive Sequential Recommendation

arXiv: https://arxiv.org/abs/2212.04120 · HTML: https://mlsanity.com/paper/2212.04120/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2022.

## Built on by (most cited first, 20 of 20)

- [Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences](https://mlsanity.com/paper/2307.14225/) (2023)
- [Diffusion Augmentation for Sequential Recommendation](https://mlsanity.com/paper/2309.12858/) (2023)
- [Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec?](https://mlsanity.com/paper/2309.07602/) (2023)
- [Toward a Foundation Model for Time Series Data](https://mlsanity.com/paper/2310.03916/) (2023)
- [Double Correction Framework for Denoising Recommendation](https://mlsanity.com/paper/2405.11272/) (2024)
- [Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogs](https://mlsanity.com/paper/2409.18721/) (2024)
- [FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Data](https://mlsanity.com/paper/2310.13818/) (2023)
- [Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders](https://mlsanity.com/paper/2507.16289/) (2025)
- [An Efficient Content-based Time Series Retrieval System](https://mlsanity.com/paper/2310.03919/) (2023)
- [Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems](https://mlsanity.com/paper/2506.23090/) (2025)
- [Adversarial Collaborative Filtering for Free](https://mlsanity.com/paper/2308.13541/) (2023)
- [A Systematic Evaluation of Generated Time Series and Their Effects in Self-Supervised Pretraining](https://mlsanity.com/paper/2408.07869/) (2024)
- [Sequential Recommendation via Adaptive Robust Attention with Multi-dimensional Embeddings](https://mlsanity.com/paper/2409.05022/) (2024)
- [Towards Lightweight Cross-domain Sequential Recommendation via External Attention-enhanced Graph Convolution Network](https://mlsanity.com/paper/2302.03221/) (2023)
- [Temporal Treasure Hunt: Content-based Time Series Retrieval System for Discovering Insights](https://mlsanity.com/paper/2311.02560/) (2023)
- [Ego-Network Transformer for Subsequence Classification in Time Series Data](https://mlsanity.com/paper/2311.02561/) (2023)
- [Enhancing Recommendation with Denoising Auxiliary Task](https://mlsanity.com/paper/2409.17402/) (2024)
- [Empowering Denoising Sequential Recommendation with Large Language Model Embeddings](https://mlsanity.com/paper/2510.04239/) (2025)
- [Precision Tracked Transformer via Kalman Filtering, Kriging and Process Noise](https://mlsanity.com/paper/2605.18832/) (2026)
- [Adapting Knowledge Graphs for Behavior Denoising in Sequential Recommendation](https://mlsanity.com/paper/2608.21243/) (2026)

## Abstract (arXiv)

Transformer-based sequential recommenders are very powerful for capturing both short-term and long-term sequential item dependencies. This is mainly attributed to their unique self-attention networks to exploit pairwise item-item interactions within the sequence. However, real-world item sequences are often noisy, which is particularly true for implicit feedback. For example, a large portion of clicks do not align well with user preferences, and many products end up with negative reviews or being returned. As such, the current user action only depends on a subset of items, not on the entire sequences. Many existing Transformer-based models use full attention distributions, which inevitably assign certain credits to irrelevant items. This may lead to sub-optimal performance if Transformers are not regularized properly.