# Time-based Sequence Model for Personalization and Recommendation Systems

arXiv: https://arxiv.org/abs/2008.11922 · HTML: https://mlsanity.com/paper/2008.11922/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2020.

## Built on by (most cited first, 11 of 11)

- [Accelerating Recommendation System Training by Leveraging Popular Choices](https://mlsanity.com/paper/2103.00686/) (2021)
- [Heterogeneous Acceleration Pipeline for Recommendation System Training](https://mlsanity.com/paper/2204.05436/) (2022)
- [BagPipe: Accelerating Deep Recommendation Model Training](https://mlsanity.com/paper/2202.12429/) (2022)
- [Differentiable NAS Framework and Application to Ads CTR Prediction](https://mlsanity.com/paper/2110.14812/) (2021)
- [FastPersist: Accelerating Model Checkpointing in Deep Learning](https://mlsanity.com/paper/2406.13768/) (2024)
- [Ad-Rec: Advanced Feature Interactions to Address Covariate-Shifts in Recommendation Networks](https://mlsanity.com/paper/2308.14902/) (2023)
- [PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models](https://mlsanity.com/paper/2406.14571/) (2024)
- [Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems](https://mlsanity.com/paper/2212.06264/) (2022)
- [Accelerating Recommender Model Training by Dynamically Skipping Stale Embeddings](https://mlsanity.com/paper/2404.04270/) (2024)
- [Stochastic Communication Avoidance for Recommendation Systems](https://mlsanity.com/paper/2411.01611/) (2024)
- [SA-RSQ: A Versatile Sparse Representation Framework for Multi-modal Recommender Systems](https://mlsanity.com/paper/2608.22979/) (2026)

## Abstract (arXiv)

In this paper we develop a novel recommendation model that explicitly incorporates time information. The model relies on an embedding layer and TSL attention-like mechanism with inner products in different vector spaces, that can be thought of as a modification of multi-headed attention. This mechanism allows the model to efficiently treat sequences of user behavior of different length. We study the properties of our state-of-the-art model on statistically designed data set. Also, we show that it outperforms more complex models with longer sequence length on the Taobao User Behavior dataset.