# Dynamic Forgetting and Spatio-Temporal Periodic Interest Modeling for Local-Life Service Recommendation

arXiv: https://arxiv.org/abs/2508.02451 · HTML: https://mlsanity.com/paper/2508.02451/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction](https://mlsanity.com/paper/2006.05639/) (2020)
- [Frequency Enhanced Hybrid Attention Network for Sequential Recommendation](https://mlsanity.com/paper/2304.09184/) (2023)
- [A Comprehensive Review of Recommender Systems: Transitioning from Theory to Practice](https://mlsanity.com/paper/2407.13699/) (2024)
- [ST-PIL: Spatial-Temporal Periodic Interest Learning for Next Point-of-Interest Recommendation](https://mlsanity.com/paper/2104.02262/) (2021)
- [Pareto-based Multi-Objective Recommender System with Forgetting Curve](https://mlsanity.com/paper/2312.16868/) (2023)
- [Adaptive Forgetting Curves for Spaced Repetition Language Learning](https://mlsanity.com/paper/2004.11327/) (2020)
- [Modeling Spatiotemporal Periodicity and Collaborative Signal for Local-Life Service Recommendation](https://mlsanity.com/paper/2309.12565/) (2023)

## Built on by (most cited first, 2 of 2)

- [MTmixAtt: Integrating Mixture-of-Experts with Multi-Mix Attention for Large-Scale Recommendation](https://mlsanity.com/paper/2510.15286/) (2025)
- [Interpretable Representation via LLM-Driven Generative Disentanglement for Local-Life Service Recommendation](https://mlsanity.com/paper/2607.27944/) (2026)

## Abstract (arXiv)

In the context of the booming digital economy, recommendation systems, as a key link connecting users and numerous services, face challenges in modeling user behavior sequences on local-life service platforms, including the sparsity of long sequences and strong spatio-temporal dependence. Such challenges can be addressed by drawing an analogy to the forgetting process in human memory. This is because users' responses to recommended content follow the recency effect and the cyclicality of memory. By exploring this, this paper introduces the forgetting curve and proposes Spatio-Temporal periodic Interest Modeling (STIM) with long sequences for local-life service recommendation. STIM integrates three key components: a dynamic masking module based on the forgetting curve, which is used to extract both recent spatiotemporal features and periodic spatiotemporal features; a query-based mixture of experts (MoE) approach that can adaptively activate expert networks under different dynamic masks, enabling the collaborative modeling of time, location, and items; and a hierarchical multi-interest network unit, which captures multi-interest representations by modeling the hierarchical interactions between the shallow and deep semantics of users' recent behaviors. By introducing the STIM method, we conducted online A/B tests and achieved a 1.54% improvement in gross transaction volume (GTV). In addition, extended offline experiments also showed improvements. STIM has been deployed in a large-scale local-life service recommendation system, serving hundreds of millions of daily active users in core application scenarios.