Cross-Domain Recommendation (CDR) and Cross-System Recommendation (CSR) have been proposed to improve the recommendation accuracy in a target dataset (domain/system) with the help of a source one (with relatively richer information). However, most existing CDR and CSR approaches are single-target, which can only help the target dataset and thus cannot benefit the source dataset. In this paper, we focus on three new scenarios, i.e., Dual-Target CDR (DTCDR), Multi-Target CDR (MTCDR), and CDR+CSR, and aim to improve the recommendation accuracy in all datasets simultaneously. To do this, we propose a unified framework, called GA (based on Graph embedding and Attention techniques), for all three scenarios. In GA, we first construct separate heterogeneous graphs to generate more representative user and item embeddings. Then, we propose an element-wise attention mechanism to effectively combine the embeddings of common entities (users/items) learned from different datasets. Moreover, to avoid negative transfer, we further propose a Personalized training strategy to minimize the embedding difference of common entities between a richer dataset and a sparser dataset, deriving GA-DTCDR-P, GA-MTCDR-P, and GA-CDR+CSR-P for the three scenarios respectively. Extensive experiments conducted on four real-world datasets demonstrate that our proposed GA models significantly outperform the state-of-the-art approaches.

Recommender systems are widely used in various online platforms. In the context of sequential recommendation, it is essential to accurately capture the chronological patterns in user activities to generate relevant recommendations. Conventional ID-based sequential recommenders have shown promise but lack comprehensive real-world knowledge about items, limiting their effectiveness. Recent advancements in Large Language Models (LLMs) offer the potential to bridge this gap by leveraging the extensive real-world knowledge encapsulated in LLMs. However, integrating LLMs into sequential recommender systems comes with its own challenges, including inadequate representation of sequential behavior patterns and long inference latency. In this paper, we propose SeRALM (Enhancing Sequential Recommenders with Augmented Knowledge from Aligned Large Language Models) to address these challenges. SeRALM integrates LLMs with conventional ID-based sequential recommenders for sequential recommendation tasks. We combine text-format knowledge generated by LLMs with item IDs and feed this enriched data into ID-based recommenders, benefitting from the strengths of both paradigms. Moreover, we develop a theoretically underpinned alignment training method to refine LLMs' generation using feedback from ID-based recommenders for better knowledge augmentation. We also present an asynchronous technique to expedite the alignment training process. Experimental results on public benchmarks demonstrate that SeRALM significantly improves the performances of ID-based sequential recommenders. Further, a series of ablation studies and analyses corroborate SeRALM's proficiency in steering LLMs to generate more pertinent and advantageous knowledge across diverse scenarios.

Large Language Models (LLMs) have shown impressive performance in natural language tasks, but their outputs can exhibit undesirable attributes or biases. Existing methods for steering LLMs toward desired attributes often assume unbiased representations and rely solely on steering prompts. However, the representations learned from pre-training can introduce semantic biases that influence the steering process, leading to suboptimal results. We propose LLMGuardrail, a novel framework that incorporates causal analysis and adversarial learning to obtain unbiased steering representations in LLMs. LLMGuardrail systematically identifies and blocks the confounding effects of biases, enabling the extraction of unbiased steering representations. Experiments demonstrate LLMGuardrail's effectiveness in steering LLMs toward desired attributes while mitigating biases. Our work contributes to developing safe and reliable LLMs that align with desired attributes.

Causal effect estimation from networked observational data encounters notable challenges, primarily hidden confounders arising from network structure, or spillover effects that influence unit's outcomes based on neighboring treatment assignments. Existing graph neural network (GNN)-based methods have endeavored to address these challenges, utilizing the GNN's message-passing mechanism to capture hidden confounders or model spillover effects. However, they mainly focus on transductive causal effect learning on a single networked data, limiting their efficacy in inductive settings for real-world applications where networked data often originates from multiple environments influenced by potentially varying time or geographical regions. In light of this, we introduce the principle of invariance to the task of causal effect estimation on networked data, culminating in our Invariant Graph Learning (IGL) framework. Specifically, it first generates multiple networked data to simulate diverse environments from a given observational data. Then it further encourages the model to learn environment-invariant representations for confounders and spillover effects. Such a design enables the model to extrapolate beyond a single observed environment, thereby improving the performance of causal effect estimation in potential new environments. Extensive experiments on two real-world datasets demonstrates the superiority of our approach.

The recent advancements in Deep Reinforcement Learning (DRL) have significantly enhanced the performance of adaptive Traffic Signal Control (TSC). However, DRL policies are typically represented by neural networks, which are over-parameterized black-box models. As a result, the learned policies often lack interpretability, and cannot be deployed directly in the real-world edge hardware due to resource constraints. In addition, the DRL methods often exhibit limited generalization performance, struggling to generalize the learned policy to other geographical regions. These factors limit the practical application of learning-based approaches. To address these issues, we suggest the use of an inherently interpretable program for representing the control policy. We present a new approach, Programmatic Interpretable reinforcement learning for traffic signal control (π-light), designed to autonomously discover non-differentiable programs. Specifically, we define a Domain Specific Language (DSL) and transformation rules for constructing programs, and utilize Monte Carlo Tree Search (MCTS) to find the optimal program in a discrete space. Extensive experiments demonstrate that our method consistently outperforms baseline approaches. Moreover, π-Light exhibits superior generalization capabilities compared to DRL, enabling training and evaluation across intersections from different cities. Finally, we analyze how the learned program policies can directly deploy on edge devices with extremely limited resources.