ADS-POI: Agentic Spatiotemporal State Decomposition for Next Point-of-Interest Recommendation

• URL: http://arxiv.org/abs/2604.20846v1
• Date: Tue, 10 Feb 2026 07:16:47 GMT
• Title: ADS-POI: Agentic Spatiotemporal State Decomposition for Next Point-of-Interest Recommendation
• Abstract summary: Next-of-interest (POI) recommendation requires modeling user mobility as atemporal sequence.<n>Most existing methods compress a user's history into a single latent representation.<n>We propose ADS-POI, atemporal state decomposition framework for next POI recommendation.
• Score: 18.263381277536357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Next point-of-interest (POI) recommendation requires modeling user mobility as a spatiotemporal sequence, where different behavioral factors may evolve at different temporal and spatial scales. Most existing methods compress a user's history into a single latent representation, which tends to entangle heterogeneous signals such as routine mobility patterns, short-term intent, and temporal regularities. This entanglement limits the flexibility of state evolution and reduces the model's ability to adapt to diverse decision contexts. We propose ADS-POI, a spatiotemporal state decomposition framework for next POI recommendation. ADS-POI represents a user with multiple parallel evolving latent sub-states, each governed by its own spatiotemporal transition dynamics. These sub-states are selectively aggregated through a context-conditioned mechanism to form the decision state used for prediction. This design enables different behavioral components to evolve at different rates while remaining coordinated under the current spatiotemporal context. Extensive experiments on three real-world benchmark datasets from Foursquare and Gowalla demonstrate that ADS-POI consistently outperforms strong state-of-the-art baselines under a full-ranking evaluation protocol. The results show that decomposing user behavior into multiple spatiotemporally aware states leads to more effective and robust next POI recommendation. Our code is available at https://github.com/YuZhenyuLindy/ADS-POI.git.

Related papers

• Think2Go: Generative Next POI Recommendation with LLM Reasoning [57.37706163990998]
  Next Point-of-Interest (POI) recommendation task focuses on mining user behavioral preference patterns from historical check-ins to provide personalized suggestions for the next destination.<n>Existing methods rely on shallow contextual information and handcrafted feature interactions to predict the next POI.<n>We propose Think2Go, a novel generative next POI recommendation framework, which enhances the model's comprehension of SID representations and explores diverse spatial-temporal patterns via test-time computational scaling.
  arXiv  Detail & Related papers  (2026-07-31T03:52:20Z)
• MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation [65.068801413044]
  Continual test-time adaptation adapts a source-pretrained model to non-stationary, unlabeled target streams.<n>We introduce MoASE, a mixture-of-experts that disentangles domain-agnostic structure from domain-specific texture.
  arXiv  Detail & Related papers  (2026-05-18T01:52:12Z)
• Discovering Coordinated Joint Options via Inter-Agent Relative Dynamics [13.0114299827121]
  In multi-agent settings, the exponential growth of the joint state space with the number of agents makes coordinated behaviours even more valuable.<n>Existing multi-agent option discovery methods often sacrifice coordination by producing loosely coupled or fully independent behaviours.<n>We propose a joint-state abstraction that compresses the state space while preserving the information necessary to discover strongly coordinated behaviours.
  arXiv  Detail & Related papers  (2025-12-31T12:39:22Z)
• Model-Based Policy Adaptation for Closed-Loop End-to-End Autonomous Driving [54.46325690390831]
  We propose Model-based Policy Adaptation (MPA), a general framework that enhances the robustness and safety of pretrained E2E driving agents during deployment.<n>MPA first generates diverse counterfactual trajectories using a geometry-consistent simulation engine.<n>MPA trains a diffusion-based policy adapter to refine the base policy's predictions and a multi-step Q value model to evaluate long-term outcomes.
  arXiv  Detail & Related papers  (2025-11-26T17:01:41Z)
• Next Interest Flow: A Generative Pre-training Paradigm for Recommender Systems by Modeling All-domain Movelines [8.895768051554162]
  We propose a novel generative pre-training paradigm for e-commerce recommender systems.<n>Our model learns to predict the Next Interest Flow, a dense vector sequence representing a user's future intent.<n>We present the All-domain Moveline Evolution Network (AMEN), a unified framework implementing our entire pipeline.
  arXiv  Detail & Related papers  (2025-10-13T12:13:17Z)
• What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context [56.590259941275434]
  RecPO is a preference optimization framework for sequential recommendation.<n>It exploits adaptive reward margins based on inferred preference hierarchies and temporal signals.<n>It mirrors key characteristics of human decision-making: favoring timely satisfaction, maintaining coherent preferences, and exercising discernment under shifting contexts.
  arXiv  Detail & Related papers  (2025-06-02T21:09:29Z)
• AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving [59.94343412438211]
  We introduce the GPT style next token motion prediction into motion prediction. Different from language data which is composed of homogeneous units -words, the elements in the driving scene could have complex spatial-temporal and semantic relations. We propose to adopt three factorized attention modules with different neighbors for information aggregation and different position encoding styles to capture their relations.
  arXiv  Detail & Related papers  (2024-03-20T06:22:37Z)
• CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning [7.12850130177239]
  We propose a Context-Aware Pseudo-Multi-Task Recommender System (CPMR) to model the evolution in both historical and contextual scenarios. Experimental results on four benchmark recommendation datasets show that CPMR consistently outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-09-09T14:07:11Z)
• BASM: A Bottom-up Adaptive Spatiotemporal Model for Online Food Ordering Service [7.088699796818076]
  Online- Ordering Service (FOS) is a popular location-based service that helps people to order what you want. Here, we propose a new Adaptive Model(StemporalM) to adaptively fit the differenttemporal data distribution. We also introduce two new metrics, Time-period-wise AUC (TAUC) and City-wise AUC (CAUC) to verify the effectiveness of BASM.
  arXiv  Detail & Related papers  (2022-11-22T06:08:57Z)
• Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning [53.18060442931179]
  We propose the age of semantics (AoS) for measuring semantics freshness of status updates in a cooperative relay communication system. We derive an online deep actor-critic (DAC) learning scheme under the on-policy temporal difference learning framework. We then put forward a novel offline DAC scheme, which estimates the optimal control policy from a previously collected dataset.
  arXiv  Detail & Related papers  (2022-09-19T11:55:28Z)
• Modelling of Bi-directional Spatio-Temporal Dependence and Users' Dynamic Preferences for Missing POI Check-in Identification [38.51964956686177]
  We develop a model, named Bi-STDDP, which can integrate bi-directional-temporal dependence and users' dynamic preferences. Results demonstrate significant improvements of our model compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-31T03:54:37Z)
• Instance-Aware Predictive Navigation in Multi-Agent Environments [93.15055834395304]
  We propose an Instance-Aware Predictive Control (IPC) approach, which forecasts interactions between agents as well as future scene structures. We adopt a novel multi-instance event prediction module to estimate the possible interaction among agents in the ego-centric view. We design a sequential action sampling strategy to better leverage predicted states on both scene-level and instance-level.
  arXiv  Detail & Related papers  (2021-01-14T22:21:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.