# Time Matters: A Novel Real-Time Long- and Short-term User Interest Model for Click-Through Rate Prediction

arXiv: https://arxiv.org/abs/2511.06213 · HTML: https://mlsanity.com/paper/2511.06213/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 32 references that are in the map.
- First posted on arXiv 9 Nov 2025.

## Builds on (most cited first, 21 of 21)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017)
- [Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09248/) (2019)
- [Learning Tree-based Deep Model for Recommender Systems](https://mlsanity.com/paper/1801.02294/) (2018)
- [Deep Session Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.06482/) (2019)
- [ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation](https://mlsanity.com/paper/1711.06632/) (2017)
- [User Behavior Retrieval for Click-Through Rate Prediction](https://mlsanity.com/paper/2005.14171/) (2020)
- [Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction](https://mlsanity.com/paper/1905.00758/) (2019)
- [SDM: Sequential Deep Matching Model for Online Large-scale Recommender System](https://mlsanity.com/paper/1909.00385/) (2019)
- [Context-aware Sequential Recommendation](https://mlsanity.com/paper/1609.05787/) (2016)
- [Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction](https://mlsanity.com/paper/1906.03776/) (2019)
- [Representation Learning-Assisted Click-Through Rate Prediction](https://mlsanity.com/paper/1906.04365/) (2019)
- [Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction](https://mlsanity.com/paper/1911.00886/) (2019)
- [Time-Aware Prospective Modeling of Users for Online Display Advertising](https://mlsanity.com/paper/1911.05100/) (2019)
- [Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution](https://mlsanity.com/paper/2001.03025/) (2020)

## Abstract (arXiv)

Click-Through Rate (CTR) prediction is a core task in online personalization platform. A key step for CTR prediction is to learn accurate user representation to capture their interests. Generally, the interest expressed by a user is time-variant, i.e., a user activates different interests at different time. However, most previous CTR prediction methods overlook the correlation between the activated interest and the occurrence time, resulting in what they actually learn is the mixture of the interests expressed by the user at all time, rather than the real-time interest at the certain prediction time. To capture the correlation between the activated interest and the occurrence time, in this paper we investigate users' interest evolution from the perspective of the whole time line and develop two regular patterns: periodic pattern and time-point pattern. Based on the two patterns, we propose a novel time-aware long- and short-term user interest modeling method to model users' dynamic interests at different time. Extensive experiments on public datasets as well as an industrial dataset verify the effectiveness of exploiting the two patterns and demonstrate the superiority of our proposed method compared with other state-of-the-art ones.