Despite the vital role of recommendation systems (RS) in delivering personalized services tailored to users' needs, user fairness issues have increasingly emerged in recent years, especially differentiated treatments caused by user sensitive attributes. This not only undermines both user experience and platform revenues, but also leads to potential social unfairness. Although many fairness-aware methods have been developed and achieved some success, many of them filter out sensitive attribute information while ignoring the potential loss of personalized information, leading to suboptimal results. Large language models (LLMs) have demonstrated remarkable capabilities across various tasks, while their potential in fairness-aware recommendation remains further unexplored. In this paper, we propose a new exploration of fairness-aware RS by prompting LLMs with the user's personalized fairness degrees to augment fair user-item interaction for training. Specifically, to estimate the fairness degree of each user, we first design a personalized unfairness modelling module, consisting of a replaceable fairness-aware representation learning model. Moreover, to enable LLMs to perceive fairness from semantic information and adapt to various scenarios, we propose a prompt tuning mechanism to optimize user-shared prompt templates with the objective of maximizing the consistency with users' preferences and the diversity of augmented data. Finally, we utilize LLMs to augment fair interaction data with the optimal prompts and integrate it with the raw data to re-train the recommendation model. Extensive experiments on two real-world datasets demonstrate the superiority of our approach in terms of recommendation performance, fairness, and robustness.

The demand for more precise and timely urban resource allocation and management has driven the extension of urban flow prediction from short-term to long-term horizons. As the time scale expands, the issue of urban flow distribution shift becomes increasingly prominent due to various impact factors, such as weather, events, city changes, etc. Traditionally, comprehensively analyzing and addressing the causal relationships underlying the distribution shift caused by these factors has been challenging. In this paper, we propose that these impact factors can be partitioned in two major types, i.e., context factors and structural factors. We then present a decomposition-based model for long-term urban flow prediction from a causal perspective, namedDeCau, which can discriminate between the two types of factors for effectively solving the problem of urban flow distribution shift. First, we employ a decomposition module to decompose urban flow into seasonal part and trend part. The seasonal part contains high frequency irregular variations caused by context factors. We advise a shared distribution estimator to approximate the unavailable prior distributions of context factors, and then apply causal intervention to mitigate the confounding impact of context factors. The distribution shift in the trend part is induced by structural factors. We design a dual causal dependency extractor to model the causality between POIs distribution and urban flow, and then eliminate spurious correlations through causal adjustment. Finally, we design an end-to-end framework for long-term urban flow prediction by combining the embeddings from two parts, enabling the model to generalize to unseen distribution. Extensive experimental results demonstrateDeCauoutperforms state-of-the-art baselines.

Point-of-Interest (POI) recommendation, an important research hotspot in the field of urban computing, plays a crucial role in urban construction. While understanding the process of users’ travel decisions and exploring the causality of POI choosing is not easy due to the complex and diverse influencing factors in urban travel scenarios. Moreover, the spurious explanations caused by severe data sparsity, i.e., misrepresenting universal relevance as causality, may also hinder us from understanding users’ travel decisions. To this end, in this article, we propose a factor-level causal explanation generation framework based on counterfactual data augmentation for user travel decisions, named Factor-level Causal Explanation for User Travel Decisions (FCE-UTD), which can distinguish between true and false causal factors and generate true causal explanations. Specifically, we first assume that a user decision is composed of a set of several different factors. Then, by preserving the user decision structure with a joint counterfactual contrastive learning paradigm, we learn the representation of factors and detect the relevant factors. Next, we further identify true causal factors by constructing counterfactual decisions with a counterfactual representation generator, in particular, it can not only augment the dataset and mitigate the sparsity but also contribute to clarifying the causal factors from other false causal factors that may cause spurious explanations. Besides, a causal dependency learner is proposed to identify causal factors for each decision by learning causal dependency scores. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our approach in terms of check-in rate, fidelity, and downstream tasks under different behavior scenarios. The extra case studies also demonstrate the ability of FCE-UTD to generate causal explanations in POI choosing.