🎯 Top Personalized Recommendations
AI Summary
- Across offline and online experiments, GESR consistently delivered improvements in topline, engagement, and consumption metrics while preserving training and inference efficiencies. [3]
- The GESR paradigm has shown promising results in improving ESR performance, demonstrating its potential to reshape the design practices within large-scale recommendation systems. [3]
- The Generative Early Stage Ranking (GESR) paradigm addresses the gap between effectiveness and efficiency in industry Early Stage Ranking models. [2]
- Early Stage Ranking (ESR): A type of recommendation system that predicts user behavior at the early stages of interaction. [1]
Abstract
Large-scale recommendations commonly adopt a multi-stage cascading ranking system paradigm to balance effectiveness and efficiency. Early Stage Ranking (ESR) systems utilize the "user-item decoupling" approach, where independently learned user and item representations are only combined at the final layer. While efficient, this design is limited in effectiveness, as it struggles to capture fine-grained user-item affinities and cross-signals. To address these, we propose the Generative Early Stage Ranking (GESR) paradigm, introducing the Mixture of Attention (MoA) module which leverages diverse attention mechanisms to bridge the effectiveness gap: the Hard Matching Attention (HMA) module encodes explicit cross-signals by computing raw match counts between user and item features; the Target-Aware Self Attention module generates target-aware user representations conditioned on the item, enabling more personalized learning; and the Cross Attention modules facilitate early and more enriched interactions between user-item features. MoA's specialized attention encodings are further refined in the final layer through a Multi-Logit Parameterized Gating (MLPG) module, which integrates the newly learned embeddings via gating and produces secondary logits that are fused with the primary logit. To address the efficiency and latency challenges, we have introduced a comprehensive suite of optimization techniques. These span from custom kernels that maximize the capabilities of the latest hardware to efficient serving solutions powered by caching mechanisms. The proposed GESR paradigm has shown substantial improvements in topline metrics, engagement, and consumption tasks, as validated by both offline and online experiments. To the best of our knowledge, this marks the first successful deployment of full target-aware attention sequence modeling within an ESR stage at such a scale.
Large-scale recommendations commonly adopt a multi-stage cascading ranking system paradigm to balance effectiveness and efficiency. Early Stage Ranking (ESR) systems utilize the "user-item decoupling" approach, where independently learned user and item representations are only combined at the final layer. While efficient, this design is limited in effectiveness, as it struggles to capture fine-grained user-item affinities and cross-signals. To address these, we propose the Generative Early Stage Ranking (GESR) paradigm, introducing the Mixture of Attention (MoA) module which leverages diverse attention mechanisms to bridge the effectiveness gap: the Hard Matching Attention (HMA) module encodes explicit cross-signals by computing raw match counts between user and item features; the Target-Aware Self Attention module generates target-aware user representations conditioned on the item, enabling more personalized learning; and the Cross Attention modules facilitate early and more enriched interactions between user-item features. MoA's specialized attention encodings are further refined in the final layer through a Multi-Logit Parameterized Gating (MLPG) module, which integrates the newly learned embeddings via gating and produces secondary logits that are fused with the primary logit. To address the efficiency and latency challenges, we have introduced a comprehensive suite of optimization techniques. These span from custom kernels that maximize the capabilities of the latest hardware to efficient serving solutions powered by caching mechanisms. The proposed GESR paradigm has shown substantial improvements in topline metrics, engagement, and consumption tasks, as validated by both offline and online experiments. To the best of our knowledge, this marks the first successful deployment of full target-aware attention sequence modeling within an ESR stage at such a scale.
Why we think this paper is great for you:
This paper directly addresses multi-stage ranking systems, which are crucial for efficiently delivering personalized recommendations. You will find its insights on early-stage ranking highly relevant to optimizing your recommendation pipelines.
This paper directly addresses multi-stage ranking systems, which are crucial for efficiently delivering personalized recommendations. You will find its insights on early-stage ranking highly relevant to optimizing your recommendation pipelines.
Abstract
Ranking models have become an important part of modern personalized recommendation systems. However, significant challenges persist in handling high-cardinality, heterogeneous, and sparse feature spaces, particularly regarding model scalability and efficiency. We identify two key bottlenecks: (i) Representation Bottleneck: Driven by the high cardinality and dynamic nature of features, model capacity is forced into sparse-activated embedding layers, leading to low-rank representations. This, in turn, triggers phenomena like "One-Epoch" and "Interaction-Collapse," ultimately hindering model scalability.(ii) Computational Bottleneck: Integrating all heterogeneous features into a unified model triggers an explosion in the number of feature tokens, rendering traditional attention mechanisms computationally demanding and susceptible to attention dispersion. To dismantle these barriers, we introduce STORE, a unified and scalable token-based ranking framework built upon three core innovations: (1) Semantic Tokenization fundamentally tackles feature heterogeneity and sparsity by decomposing high-cardinality sparse features into a compact set of stable semantic tokens; and (2) Orthogonal Rotation Transformation is employed to rotate the subspace spanned by low-cardinality static features, which facilitates more efficient and effective feature interactions; and (3) Efficient attention that filters low-contributing tokens to improve computional efficiency while preserving model accuracy. Across extensive offline experiments and online A/B tests, our framework consistently improves prediction accuracy(online CTR by 2.71%, AUC by 1.195%) and training effeciency (1.84 throughput).
Ranking models have become an important part of modern personalized recommendation systems. However, significant challenges persist in handling high-cardinality, heterogeneous, and sparse feature spaces, particularly regarding model scalability and efficiency. We identify two key bottlenecks: (i) Representation Bottleneck: Driven by the high cardinality and dynamic nature of features, model capacity is forced into sparse-activated embedding layers, leading to low-rank representations. This, in turn, triggers phenomena like "One-Epoch" and "Interaction-Collapse," ultimately hindering model scalability.(ii) Computational Bottleneck: Integrating all heterogeneous features into a unified model triggers an explosion in the number of feature tokens, rendering traditional attention mechanisms computationally demanding and susceptible to attention dispersion. To dismantle these barriers, we introduce STORE, a unified and scalable token-based ranking framework built upon three core innovations: (1) Semantic Tokenization fundamentally tackles feature heterogeneity and sparsity by decomposing high-cardinality sparse features into a compact set of stable semantic tokens; and (2) Orthogonal Rotation Transformation is employed to rotate the subspace spanned by low-cardinality static features, which facilitates more efficient and effective feature interactions; and (3) Efficient attention that filters low-contributing tokens to improve computional efficiency while preserving model accuracy. Across extensive offline experiments and online A/B tests, our framework consistently improves prediction accuracy(online CTR by 2.71%, AUC by 1.195%) and training effeciency (1.84 throughput).
Why we think this paper is great for you:
This research focuses on scaling up ranking models for personalized recommendation systems, tackling challenges in efficiency and feature handling. It offers valuable strategies for improving the performance of your recommendation engines.
This research focuses on scaling up ranking models for personalized recommendation systems, tackling challenges in efficiency and feature handling. It offers valuable strategies for improving the performance of your recommendation engines.
AI Summary
- It has two core components: a simple data augmentation scheme and a probabilistic objective that exploits the extended distribution while avoiding representation collapse. [3]
- The effectiveness of ELBO TDS depends on the quality of the data augmentation scheme and the choice of hyperparameters. [3]
- Self-supervised learning has emerged as a powerful paradigm for representation learning in recommender systems. [3]
- ELBO TDS aims to improve robustness in dynamic recommender systems by mimicking temporal fluctuations and avoiding representation collapse. [3]
- ELBO TDS is a new framework that helps make recommender systems more robust and accurate. [3]
- It does this by mimicking the way user behavior changes over time and avoiding problems with representation collapse. [3]
- The paper proposes a probabilistic framework, ELBO TDS, that improves robustness in dynamic recommender systems by mimicking temporal fluctuations and avoiding representation collapse. [2]
- The proposed ELBO TDS framework is lightweight, plug-and-play, and improves robustness in dynamic recommender systems. [1]
Abstract
Temporal distribution shift (TDS) erodes the long-term accuracy of recommender systems, yet industrial practice still relies on periodic incremental training, which struggles to capture both stable and transient patterns. Existing approaches such as invariant learning and self-supervised learning offer partial solutions but often suffer from unstable temporal generalization, representation collapse, or inefficient data utilization. To address these limitations, we propose ELBO$_\text{TDS}$, a probabilistic framework that integrates seamlessly into industry-scale incremental learning pipelines. First, we identify key shifting factors through statistical analysis of real-world production data and design a simple yet effective data augmentation strategy that resamples these time-varying factors to extend the training support. Second, to harness the benefits of this extended distribution while preventing representation collapse, we model the temporal recommendation scenario using a causal graph and derive a self-supervised variational objective, ELBO$_\text{TDS}$, grounded in the causal structure. Extensive experiments supported by both theoretical and empirical analysis demonstrate that our method achieves superior temporal generalization, yielding a 2.33\% uplift in GMV per user and has been successfully deployed in Shopee Product Search. Code is available at https://github.com/FuCongResearchSquad/ELBO4TDS.
Temporal distribution shift (TDS) erodes the long-term accuracy of recommender systems, yet industrial practice still relies on periodic incremental training, which struggles to capture both stable and transient patterns. Existing approaches such as invariant learning and self-supervised learning offer partial solutions but often suffer from unstable temporal generalization, representation collapse, or inefficient data utilization. To address these limitations, we propose ELBO$_\text{TDS}$, a probabilistic framework that integrates seamlessly into industry-scale incremental learning pipelines. First, we identify key shifting factors through statistical analysis of real-world production data and design a simple yet effective data augmentation strategy that resamples these time-varying factors to extend the training support. Second, to harness the benefits of this extended distribution while preventing representation collapse, we model the temporal recommendation scenario using a causal graph and derive a self-supervised variational objective, ELBO$_\text{TDS}$, grounded in the causal structure. Extensive experiments supported by both theoretical and empirical analysis demonstrate that our method achieves superior temporal generalization, yielding a 2.33\% uplift in GMV per user and has been successfully deployed in Shopee Product Search. Code is available at https://github.com/FuCongResearchSquad/ELBO4TDS.
Why we think this paper is great for you:
This paper explores methods to maintain the accuracy of recommender systems in industrial settings despite temporal shifts. Its focus on long-term accuracy is essential for robust, real-world recommendation solutions.
This paper explores methods to maintain the accuracy of recommender systems in industrial settings despite temporal shifts. Its focus on long-term accuracy is essential for robust, real-world recommendation solutions.
Abstract
Recommender systems shape how people discover information, form opinions, and connect with society. Yet, as their influence grows, traditional metrics, e.g., accuracy, clicks, and engagement, no longer capture what truly matters to humans. The workshop on Human-Centered Recommender Systems (HCRS) calls for a paradigm shift from optimizing engagement toward designing systems that truly understand, involve, and benefit people. It brings together researchers in recommender systems, human-computer interaction, AI safety, and social computing to explore how human values, e.g., trust, safety, fairness, transparency, and well-being, can be integrated into recommendation processes. Centered around three thematic axes-Human Understanding, Human Involvement, and Human Impact-HCRS features keynotes, panels, and papers covering topics from LLM-based interactive recommenders to societal welfare optimization. By fostering interdisciplinary collaboration, HCRS aims to shape the next decade of responsible and human-aligned recommendation research.
Recommender systems shape how people discover information, form opinions, and connect with society. Yet, as their influence grows, traditional metrics, e.g., accuracy, clicks, and engagement, no longer capture what truly matters to humans. The workshop on Human-Centered Recommender Systems (HCRS) calls for a paradigm shift from optimizing engagement toward designing systems that truly understand, involve, and benefit people. It brings together researchers in recommender systems, human-computer interaction, AI safety, and social computing to explore how human values, e.g., trust, safety, fairness, transparency, and well-being, can be integrated into recommendation processes. Centered around three thematic axes-Human Understanding, Human Involvement, and Human Impact-HCRS features keynotes, panels, and papers covering topics from LLM-based interactive recommenders to societal welfare optimization. By fostering interdisciplinary collaboration, HCRS aims to shape the next decade of responsible and human-aligned recommendation research.
Why we think this paper is great for you:
This workshop highlights the importance of designing recommender systems that truly cater to human needs beyond traditional metrics. You will appreciate its emphasis on creating more meaningful and user-centric experiences.
This workshop highlights the importance of designing recommender systems that truly cater to human needs beyond traditional metrics. You will appreciate its emphasis on creating more meaningful and user-centric experiences.
Abstract
This paper introduces fly-by transit (FBT), a novel mobility system that employs modular mini-electric vehicles (mini-EVs) to provide door-to-door shared mobility with minimal stops. Unlike existing modular minibus concepts that rely on in-motion coupling and passenger transfers -- technologies unlikely to mature soon -- FBT lowers the technological barriers by building upon near-term feasible solutions. The system comprises two complementary mini-EV modules: low-cost trailers for on-demand feeder trips and high-performance leaders that guide coupled trailers in high-speed platoons along trunk lines. Trailers operate independently for detour-free feeder services, while stationary coupling at designated hubs enables platoons to achieve economies of scale (EoS). In-motion decoupling of the tail trailer allows stop-less operation without delaying the main convoy. As a proof of concept, a stylized corridor model is developed to analyze optimal FBT design. Results indicate that FBT can substantially reduce travel times relative to conventional buses and lower operating costs compared with e-hailing taxis. Numerical analyses further demonstrate that FBT achieves stronger EoS than both buses and taxis, yielding more than 13\% savings in generalized system costs. By addressing key limitations of existing transit systems, this study establishes FBT as a practical and scalable pathway toward transformative urban mobility and outlines directions for future research.
This paper introduces fly-by transit (FBT), a novel mobility system that employs modular mini-electric vehicles (mini-EVs) to provide door-to-door shared mobility with minimal stops. Unlike existing modular minibus concepts that rely on in-motion coupling and passenger transfers -- technologies unlikely to mature soon -- FBT lowers the technological barriers by building upon near-term feasible solutions. The system comprises two complementary mini-EV modules: low-cost trailers for on-demand feeder trips and high-performance leaders that guide coupled trailers in high-speed platoons along trunk lines. Trailers operate independently for detour-free feeder services, while stationary coupling at designated hubs enables platoons to achieve economies of scale (EoS). In-motion decoupling of the tail trailer allows stop-less operation without delaying the main convoy. As a proof of concept, a stylized corridor model is developed to analyze optimal FBT design. Results indicate that FBT can substantially reduce travel times relative to conventional buses and lower operating costs compared with e-hailing taxis. Numerical analyses further demonstrate that FBT achieves stronger EoS than both buses and taxis, yielding more than 13\% savings in generalized system costs. By addressing key limitations of existing transit systems, this study establishes FBT as a practical and scalable pathway toward transformative urban mobility and outlines directions for future research.
Why we think this paper is great for you:
This paper introduces an innovative shared mobility system designed for efficient door-to-door travel. It presents a compelling new approach to transportation that could significantly impact future travel planning.
This paper introduces an innovative shared mobility system designed for efficient door-to-door travel. It presents a compelling new approach to transportation that could significantly impact future travel planning.
AI Summary
- The study evaluates the feasibility of Urban Air Mobility (UAM) service deployment in the San Francisco Bay Area, focusing on demand realization, fleet throughput, passenger wait-time analysis, route-level service distribution, and aircraft utilization. [2]
Abstract
Urban Air mobility has gained momentum with recent advancements in the electric vertical take-off and landing (eVTOL) vehicles, offering faster point-to-point air taxi services that could help relieve traffic congestion in chronically overburdened cities. The research assesses the feasibility and systems-of-systems level adoption potential of UAM operations in the San Francisco Bay Area by comparing passenger departure, waiting, travel, and arrival times across key regional nodes, including San Francisco, Oakland, San Jose, and Palo Alto airports, with conventional ground transportation. A multi-agent simulation was developed in MATLAB to evaluate the fleet operations and to model demand arrival using a Poisson process under stochastic passenger flows and turnaround constraints. Results indicate that utilizing UAM during peak demand could reduce total travel times up to eighty percent across the region. The findings of this paper highlight the critical operational factors for fleet schedule optimization. Especially how the fleet size, passengers' request volumes, and turnaround time directly influence waiting time, operating cost, and overall user acceptance.
Urban Air mobility has gained momentum with recent advancements in the electric vertical take-off and landing (eVTOL) vehicles, offering faster point-to-point air taxi services that could help relieve traffic congestion in chronically overburdened cities. The research assesses the feasibility and systems-of-systems level adoption potential of UAM operations in the San Francisco Bay Area by comparing passenger departure, waiting, travel, and arrival times across key regional nodes, including San Francisco, Oakland, San Jose, and Palo Alto airports, with conventional ground transportation. A multi-agent simulation was developed in MATLAB to evaluate the fleet operations and to model demand arrival using a Poisson process under stochastic passenger flows and turnaround constraints. Results indicate that utilizing UAM during peak demand could reduce total travel times up to eighty percent across the region. The findings of this paper highlight the critical operational factors for fleet schedule optimization. Especially how the fleet size, passengers' request volumes, and turnaround time directly influence waiting time, operating cost, and overall user acceptance.
Why we think this paper is great for you:
This research investigates the potential of urban air mobility to enhance travel efficiency and alleviate congestion. It offers forward-looking insights into advanced transportation solutions for urban environments.
This research investigates the potential of urban air mobility to enhance travel efficiency and alleviate congestion. It offers forward-looking insights into advanced transportation solutions for urban environments.
AI Summary
- The text appears to be a mathematical proof or derivation from the field of probability theory. [3]
- It discusses various concepts such as Laplace distributions, random variables, and disintegration theorems. [3]
- The text appears to be based on recent research in optimal transport and probability theory. [3]
- It cites various mathematical results and theorems, which suggests that it is a cutting-edge contribution to the field. [3]
- The author shows that even with this randomness, the algorithm still produces an optimal solution. [3]
- Lapd(η2Σ): a symmetric multivariate Laplace distribution in Rd with parameter η2Σ F: a set of functions ( likely a function space) that is compact under the uniform topology Γ(α, β): the set of all couplings between two probability measures α and β gξη: a function defined as gξη(x) = f(x) + ⟨ξη, x⟩ The proof establishes that for almost every ξη, the function gξη is injective on a set of full measure. [2]
- The author uses advanced mathematical notation and terminology, which may be unfamiliar to non-experts. [1]
Abstract
Optimal Transport (OT) offers a powerful framework for finding correspondences between distributions and addressing matching and alignment problems in various areas of computer vision, including shape analysis, image generation, and multimodal tasks. The computation cost of OT, however, hinders its scalability. Slice-based transport plans have recently shown promise for reducing the computational cost by leveraging the closed-form solutions of 1D OT problems. These methods optimize a one-dimensional projection (slice) to obtain a conditional transport plan that minimizes the transport cost in the ambient space. While efficient, these methods leave open the question of whether learned optimal slicers can transfer to new distribution pairs under distributional shift. Understanding this transferability is crucial in settings with evolving data or repeated OT computations across closely related distributions. In this paper, we study the min-Sliced Transport Plan (min-STP) framework and investigate the transferability of optimized slicers: can a slicer trained on one distribution pair yield effective transport plans for new, unseen pairs? Theoretically, we show that optimized slicers remain close under slight perturbations of the data distributions, enabling efficient transfer across related tasks. To further improve scalability, we introduce a minibatch formulation of min-STP and provide statistical guarantees on its accuracy. Empirically, we demonstrate that the transferable min-STP achieves strong one-shot matching performance and facilitates amortized training for point cloud alignment and flow-based generative modeling.
Optimal Transport (OT) offers a powerful framework for finding correspondences between distributions and addressing matching and alignment problems in various areas of computer vision, including shape analysis, image generation, and multimodal tasks. The computation cost of OT, however, hinders its scalability. Slice-based transport plans have recently shown promise for reducing the computational cost by leveraging the closed-form solutions of 1D OT problems. These methods optimize a one-dimensional projection (slice) to obtain a conditional transport plan that minimizes the transport cost in the ambient space. While efficient, these methods leave open the question of whether learned optimal slicers can transfer to new distribution pairs under distributional shift. Understanding this transferability is crucial in settings with evolving data or repeated OT computations across closely related distributions. In this paper, we study the min-Sliced Transport Plan (min-STP) framework and investigate the transferability of optimized slicers: can a slicer trained on one distribution pair yield effective transport plans for new, unseen pairs? Theoretically, we show that optimized slicers remain close under slight perturbations of the data distributions, enabling efficient transfer across related tasks. To further improve scalability, we introduce a minibatch formulation of min-STP and provide statistical guarantees on its accuracy. Empirically, we demonstrate that the transferable min-STP achieves strong one-shot matching performance and facilitates amortized training for point cloud alignment and flow-based generative modeling.
Why we think this paper is great for you:
This work presents a powerful framework for matching and alignment problems, which are fundamental to personalizing experiences and finding optimal correspondences. You may find its techniques useful for enhancing the underlying mechanisms of your systems.
This work presents a powerful framework for matching and alignment problems, which are fundamental to personalizing experiences and finding optimal correspondences. You may find its techniques useful for enhancing the underlying mechanisms of your systems.
Travel Planning
Abstract
Diffusion planning is a promising method for learning high-performance policies from offline data. To avoid the impact of discrepancies between planning and reality on performance, previous works generate new plans at each time step. However, this incurs significant computational overhead and leads to lower decision frequencies, and frequent plan switching may also affect performance. In contrast, humans might create detailed short-term plans and more general, sometimes vague, long-term plans, and adjust them over time. Inspired by this, we propose the Temporal Diffusion Planner (TDP) which improves decision efficiency by distributing the denoising steps across the time dimension. TDP begins by generating an initial plan that becomes progressively more vague over time. At each subsequent time step, rather than generating an entirely new plan, TDP updates the previous one with a small number of denoising steps. This reduces the average number of denoising steps, improving decision efficiency. Additionally, we introduce an automated replanning mechanism to prevent significant deviations between the plan and reality. Experiments on D4RL show that, compared to previous works that generate new plans every time step, TDP improves the decision-making frequency by 11-24.8 times while achieving higher or comparable performance.
Diffusion planning is a promising method for learning high-performance policies from offline data. To avoid the impact of discrepancies between planning and reality on performance, previous works generate new plans at each time step. However, this incurs significant computational overhead and leads to lower decision frequencies, and frequent plan switching may also affect performance. In contrast, humans might create detailed short-term plans and more general, sometimes vague, long-term plans, and adjust them over time. Inspired by this, we propose the Temporal Diffusion Planner (TDP) which improves decision efficiency by distributing the denoising steps across the time dimension. TDP begins by generating an initial plan that becomes progressively more vague over time. At each subsequent time step, rather than generating an entirely new plan, TDP updates the previous one with a small number of denoising steps. This reduces the average number of denoising steps, improving decision efficiency. Additionally, we introduce an automated replanning mechanism to prevent significant deviations between the plan and reality. Experiments on D4RL show that, compared to previous works that generate new plans every time step, TDP improves the decision-making frequency by 11-24.8 times while achieving higher or comparable performance.
AI Summary
- The paper proposes a new method for offline reinforcement learning called Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning. [3]
- The method can be used to improve the performance of multi-task reinforcement learning agents. [3]
- The paper cites several previous works on offline reinforcement learning, including Denoising Diffusion Probabilistic Models and Classifier-Free Diffusion Guidance. [3]
- It also mentions other methods for multi-task reinforcement learning, such as Hierarchical Diffusion for Offline Decision Making and Pareto Policy Pool for Model-Based Offline Reinforcement Learning. [3]
- The paper proposes a new method for offline reinforcement learning called Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning, which uses a diffusion model to plan and synthesize data for multi-task reinforcement learning agents. [3]
- The paper proposes a new method for offline reinforcement learning that uses a diffusion model to plan and synthesize data. [3]
- This can help improve the performance of multi-task reinforcement learning agents, but it requires significant computational resources and assumes access to large amounts of data. [3]
- The proposed method, Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning, outperforms other methods in terms of planning efficiency and data synthesis quality. [2]
- Diffusion model: A type of probabilistic model that can be used for planning and data synthesis in multi-task reinforcement learning. [1]
Travel
Abstract
Modelling migration is complicated, as people move for many reasons. Some leave their country for the first time, others return to places they once called home, or move on to new destinations. However, most models focus only on who arrives, missing the full picture of how migrant populations evolve. We introduce a model for diaspora flows that estimates both arrivals and exits using daily migration flow rates, disaggregated by age and nationality. Drawing on high-resolution administrative data from Austria covering over 1.8 million foreign nationals, the model allocates these movements across more than 2,000 municipalities based on the size of local diaspora communities. We find that exits are not exceptions but a consistent and predictable feature across all groups. Migration rejuvenates Austria's population, as both arriving and departing migrants are younger than the average resident. This effect has distinct age-geography patterns: younger migrants are drawn to cities, while older migrants are more evenly distributed in the country. By capturing both arrivals and exits simultaneously, our approach provides a more comprehensive and interpretable picture of migration dynamics, how populations change over time, and how they are influenced by space, age, and national origins.
Modelling migration is complicated, as people move for many reasons. Some leave their country for the first time, others return to places they once called home, or move on to new destinations. However, most models focus only on who arrives, missing the full picture of how migrant populations evolve. We introduce a model for diaspora flows that estimates both arrivals and exits using daily migration flow rates, disaggregated by age and nationality. Drawing on high-resolution administrative data from Austria covering over 1.8 million foreign nationals, the model allocates these movements across more than 2,000 municipalities based on the size of local diaspora communities. We find that exits are not exceptions but a consistent and predictable feature across all groups. Migration rejuvenates Austria's population, as both arriving and departing migrants are younger than the average resident. This effect has distinct age-geography patterns: younger migrants are drawn to cities, while older migrants are more evenly distributed in the country. By capturing both arrivals and exits simultaneously, our approach provides a more comprehensive and interpretable picture of migration dynamics, how populations change over time, and how they are influenced by space, age, and national origins.
AI Summary
- The study uses high-resolution administrative data from Austria's Ministry of the Interior, covering the years 2023 and 2024. [3]
- The dataset includes around 1.8 million individuals and contains key variables such as citizenship, age, gender, and municipality of residence. [3]
- The study finds that young migrants are more likely to arrive in Austria than older migrants, while older migrants are more likely to leave the country. [3]
- The study's findings have implications for policymakers seeking to understand and manage international migration in Austria. [3]
- The study suggests that policies aimed at attracting young migrants may be effective in promoting economic growth, while policies aimed at retaining older migrants may be necessary to address demographic challenges. [3]
- The study also finds that migrants from certain countries, such as Turkey and Iraq, are more likely to arrive in Austria than others. [2]
- The model estimates daily arrival and exit rates for each migrant group using a Poisson process, treating migration as a count-based event that occurs over time. [1]
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