In modern cities, there is an increasing trend for the development of business agglomeration, which can foster the prosperity of individual businesses by clustering stores and industries. Recently, the advent of Point-of-Interest (POI) data enables a new paradigm for studying the causal effect of business agglomeration in a data-driven way. To this end, we aim to quantify the contribution of the agglomeration effect to the check-in volume at POIs. This is a non-trivial causal effect estimation task due to the higher-order spatial interference typically exhibited by the agglomeration distribution. Moreover, the confounding bias can be exacerbated due to the complex spatial and functional properties inherent to confounders. Therefore, we propose a Causal effect estimation framework for AgglomeRation Effect (CARE) measurement, which includes a Spatial Interference Diffusion Network (SIDN) and a Disentangled Propensity Estimator (DPE) . SIDN captures spatial interference by spreading the treatment effect among POIs through a dedicated spatial agglomeration hypergraph. Then, DPE models a POI’s propensity of receiving the treatment and further unravels the spatial and inherent aspects of propensity by disentangled learning objectives. In addition, we incorporate SIDN and DPE into a unified causal effect estimation architecture using neural Robinson decomposition. Finally, extensive experiments on three real-world datasets validate the effectiveness and universality of CARE for measuring the agglomeration effect.

Cross-Domain Sequential Recommendation (CDSR) aims to predict users’ preferences based on historical sequential interactions across multiple domains. Existing works focus on the overlapped users who interact in multiple domains to capture the cross-domain correlations. These methods often underperform in practical scenarios featuring both overlapped and non-overlapped users due to the limited cross-domain interactions and knowledge transfer misalignment for non-overlapped users. To address this, we leverage Large Language Models (LLMs) to facilitate CDSR by fully exploiting single-domain interactions. However, LLMs exhibit inherent limitations in handling extensive item repositories and sequential collaborative signals. Moreover, the generation reliability is compromised by the hallucination problem, potentially causing noisy and unstable outputs. To this end, we propose a novel LLMCDSR framework, which employs LLMs to predict unobserved cross-domain interactions, termed pseudo items, within single-domain interactions. Specifically, we first prompt LLMs to execute the Candidate-Free Cross-Domain Interaction Generation task. Then, we devise a Collaborative-Textual Contrastive Pre-Training strategy, learning to infuse collaborative information into textual features. Afterwards, we present a novel Relevance-Aware Meta Recall Network (RMRN) to selectively identify and retrieve high-quality pseudo items from the dataset, where the parameters are optimized in a meta-learning manner. Finally, extensive experiments on two public datasets validate the effectiveness of LLMCDSR in enhancing CDSR. The code and data are available at https://github.com/xhran2010/LLMCDSR .

Electric Vehicle (EV) has become preferable choices in modern transportation system due to its environmental and energy sustainability. However, in many large cities, EV drivers often fail to find proper spots for charging because of the limited charging infrastructures and spatiotemporally unbalanced charging demands. Indeed, the recent emergence of deep reinforcement learning provides great potential to improve charging experience over long-term horizons. In this paper, we propose RLCharge for intelligent EV charging station recommendation by jointly considering various long-term spatiotemporal factors. Specifically, by regarding each charging station as an agent, we formulate the problem as a multi-objective multi-agent reinforcement learning task. We first develop a multi-agent actor-critic framework with centralized training decentralized execution. Particularly, we propose a tailor designed centralized attentive critic with the delayed access strategy to coordinate the recommendation between geo-distributed agents during centralized training. Besides, we propose the spatio-temporal heterogeneous graph convolution module to handle the partial observability problem during decentralized execution. After that, to effectively optimize multiple divergent objectives, we develop a dynamic gradient re-weighting strategy to adaptively guide the optimization direction, and propose an adaptive imitation learning scheme to further accelerate and stabilize the policy convergence. Finally, extensive experiments on two real-world datasets demonstrate that RLCHARGE achieves the best comprehensive performance compared with ten baseline approaches.

Pre-travel out-of-town recommendation aims to recommend Point-of-Interests (POIs) to the users who plan to travel out of their hometown in the near future yet have not decided where to go, i.e., their destination regions and POIs both remain unknown. It is a non-trivial task since the searching space is vast, which may lead to distinct travel experiences in different out-of-town regions and eventually confuse decision-making. Besides, users' out-of-town travel behaviors are affected not only by their personalized preferences but heavily by others' travel behaviors. To this end, we propose a Crowd-Aware Pre-Travel Out-of-town Recommendation framework (CAPTOR) consisting of two major modules: spatial-affined conditional random field (SA-CRF) and crowd behavior memory network (CBMN). Specifically, SA-CRF captures the spatial affinity among POIs while preserving the inherent information of POIs. Then, CBMN is proposed to maintain the crowd travel behaviors w.r.t. each region through three affiliated blocks reading and writing the memory adaptively. We devise the elaborated metric space with a dynamic mapping mechanism, where the users and POIs are distinguishable both inherently and geographically. Extensive experiments on two real-world nationwide datasets validate the effectiveness of CAPTOR against the pre-travel out-of-town recommendation task.

Out-of-town recommendation is designed for those users who leave their home-town areas and visit the areas they have never been to before. It is challenging to recommend Point-of-Interests (POIs) for out-of-town users since the out-of-town check-in behavior is determined by not only the user’s home-town preference but also the user’s travel intention. Besides, the user’s travel intentions are complex and dynamic, which leads to big difficulties in understanding such intentions precisely. In this paper, we propose a TRAvel-INtention-aware Out-of-town Recommendation framework, named TRAINOR. The proposed TRAINOR framework distinguishes itself from existing out-of-town recommenders in three aspects. First, graph neural networks are explored to represent users’ home-town check-in preference and geographical constraints in out-of-town check-in behaviors. Second, a user-specific travel intention is formulated as an aggregation combining home-town preference and generic travel intention together, where the generic travel intention is regarded as a mixture of inherent intentions that can be learned by Neural Topic Model (NTM). Third, a non-linear mapping function, as well as a matrix factorization method, are employed to transfer users’ home-town preference and estimate out-of-town POI’s representation, respectively. Extensive experiments on real-world data sets validate the effectiveness of the TRAINOR framework. Moreover, the learned travel intention can deliver meaningful explanations for understanding a user’s travel purposes.

Electric Vehicle (EV) has become a preferable choice in the modern transportation system due to its environmental and energy sustainability. However, in many large cities, EV drivers often fail to find the proper spots for charging, because of the limited charging infrastructures and the spatiotemporally unbalanced charging demands. Indeed, the recent emergence of deep reinforcement learning provides great potential to improve the charging experience from various aspects over a long-term horizon. In this paper, we propose a framework, named Multi-Agent Spatio-Temporal Reinforcement Learning (Master), for intelligently recommending public accessible charging stations by jointly considering various long-term spatiotemporal factors. Specifically, by regarding each charging station as an individual agent, we formulate this problem as a multi-objective multi-agent reinforcement learning task. We first develop a multi-agent actor-critic framework with the centralized attentive critic to coordinate the recommendation between geo-distributed agents. Moreover, to quantify the influence of future potential charging competition, we introduce a delayed access strategy to exploit the knowledge of future charging competition during training. After that, to effectively optimize multiple learning objectives, we extend the centralized attentive critic to multi-critics and develop a dynamic gradient re-weighting strategy to adaptively guide the optimization direction. Finally, extensive experiments on two real-world datasets demonstrate that Master achieves the best comprehensive performance compared with nine baseline approaches.