Large Language Models (LLMs) have achieved impressive reasoning abilities, but struggle with temporal understanding, especially when questions involve multiple entities, compound operators, and evolving event sequences. Temporal Knowledge Graphs (TKGs), which capture vast amounts of temporal facts in a structured format, offer a reliable source for temporal reasoning. However, existing TKG-based LLM reasoning methods still struggle with four major challenges: maintaining temporal faithfulness in multi-hop reasoning, achieving multi-entity temporal synchronization, adapting retrieval to diverse temporal operators, and reusing prior reasoning experience for stability and efficiency. To address these issues, we propose MemoTime, a memory-augmented temporal knowledge graph framework that enhances LLM reasoning through structured grounding, recursive reasoning, and continual experience learning. MemoTime decomposes complex temporal questions into a hierarchical Tree of Time, enabling operator-aware reasoning that enforces monotonic timestamps and co-constrains multiple entities under unified temporal bounds. A dynamic evidence retrieval layer adaptively selects operator-specific retrieval strategies, while a self-evolving experience memory stores verified reasoning traces, toolkit decisions, and sub-question embeddings for cross-type reuse. Comprehensive experiments on multiple temporal QA benchmarks show that MemoTime achieves overall state-of-the-art results, outperforming the strong baseline by up to 24.0%. Furthermore, MemoTime enables smaller models (e.g., Qwen3-4B) to achieve reasoning performance comparable to that of GPT-4-Turbo.

Temporal hypergraph representation learning is a concept that integrates high-order structure learning with temporal dynamics, enabling more accurate analysis of temporal and high-order interactions. To enhance model expressiveness, the latest work samples multi-hop hyperedge-centric neighbors directly from temporal hypergraphs and encodes them for high-order structure learning, achieving promising performance. Such modeling, however, incurs prohibitive computational complexity, which increases exponentially with model depth and quadratically with average hyperedge cardinality, thereby limiting model scalability. In this paper, we propose FastHeP, a fast and accurate approach for temporal hyperedge prediction, which can handle large temporal hypergraphs. The key idea is to minimize computational complexity while maintaining model expressiveness. Concretely, we design an online hyperedge-centric neighbor store, which can store time-aware and redundancy-aware neighbors for nodes with rational theoretical guarantees. Upon the neighbor store, we propose a novel hybrid message passing to model temporal high-order structures, theoretically preserving strong expressive power. This explicitly learns local high-order structures for nodes of each hyperedge via graph attention, generating the node-wise structure features. These structure features are then fused into global correlations modeling among hyperedges, with a theoretical guarantee of permutation invariance. Last, FastHeP leverages local and global high-order semantics to generate temporal hyperedge embeddings, which is efficient in a linear complexity w.r.t. model depth and average hyperedge cardinality. Extensive experiments show that FastHeP achieves up to two orders of magnitude speed-up against baselines, with an average accuracy improvement of 5.1%.

Large Language Models (LLMs) have achieved impressive results in various tasks but struggle with hallucination problems and lack of relevant knowledge, especially in deep complex reasoning and knowledge-intensive tasks.Knowledge Graphs (KGs), which capture vast amounts of facts in a structured format, offer a reliable source of knowledge for reasoning.However, existing KG-based LLM reasoning methods face challenges like handling multi-hop reasoning, multi-entity questions, and effectively utilizing graph structures.To address these issues, we propose Paths-over-Graph (PoG), a novel method that enhances LLM reasoning by integrating knowledge reasoning paths from KGs, improving the interpretability and faithfulness of LLM outputs.PoG tackles multi-hop and multi-entity questions through a three-phase dynamic multi-hop path exploration, which combines the inherent knowledge of LLMs with factual knowledge from KGs.In order to improve the efficiency, PoG prunes irrelevant information from the graph exploration first and introduces efficient three-step pruning techniques that incorporate graph structures, LLM prompting, and a pre-trained language model (e.g., SBERT) to effectively narrow down the explored candidate paths.This ensures all reasoning paths contain highly relevant information captured from KGs, making the reasoning faithful and interpretable in problem-solving.PoG innovatively utilizes graph structure to prune the irrelevant noise and represents the first method to implement multi-entity deep path detection on KGs for LLM reasoning tasks.Comprehensive experiments on five benchmark KGQA datasets demonstrate PoG outperforms the stateof-the-art method ToG across GPT-3.5-Turbo and GPT-4, achieving an average accuracy improvement of 18.9%.Notably, PoG with GPT-3.5-Turbosurpasses ToG with GPT-4 by up to 23.9%.

Missing data imputation, which aims to impute the missing values in the raw datasets, is crucial for modern data-driven models like large language models (LLMs). Despite its importance, existing solutions either 1) only support numerical and categorical data or 2) show an unsatisfactory performance due to their design prioritizing text data and overlooking intrinsic characteristics of tabular data. In this paper, we propose UnIMP, a Un ified IMP utation framework that leverages LLM and high-order message passing to enhance the imputation of mixed-type data, including numerical, categorical, and text data. Specifically, we first introduce a cell-oriented hypergraph to model the table. We then propose BiHMP, an efficient Bidirectional High-order Message-Passing network to aggregate global-local and high-order information while capturing the inter-column heterogeneity and intra-column homogeneity. To align the capacity of the LLM with the information aggregated by BiHMP, we introduce Xfusion, which, together with BiHMP, acts as adapters for the LLM. We follow a pre-training and fine-tuning pipeline to train UnIMP, integrating two optimizations: chunking technique, which divides tables into smaller chunks to enhance efficiency; and progressive masking technique, which gradually adapts the model to learn more complex data patterns. Both theoretical proofs and empirical experiments on 10 real-world datasets highlight the superiority of UnIMP over existing techniques.

The inclusion of the images opens up a security vulnerability of visually-aware recommender systems (VARSs). It can be exploited by unscrupulous parties to upload well-crafted adversarial images for certain malicious purposes (e.g., promoting their own products for profits). Some studies have focused on attacking VARSs to gain insights into their robustness, while they are still far from practical, i.e., the attacks often 1) lack diversity in perturbations, 2) are easily perceived and 3) have limited transferability, which may lead to overestimation of defenses in practice. To tackle the problems, we propose to perturb the style of the product, which is an unnoticeable but important property of visual recommendations. Specifically, we propose a novel Style perturbation-based Practical Attack Framework (SPAF). Unlike existing attacks that change pixels within l∞ -norm constraints, SPAF interferes with styles in latent feature space so that the attack becomes unbounded in the pixel space to reflect possible actual perturbations. SPAF formulates attack objectives as an optimization problem and adopts an adaptive adversarial style transfer network to solve it so that transferable and imperceptible attacks can be generated. Comprehensive experiments on real-world datasets demonstrate that SPAF significantly outperforms state-of-the-art attacks.

Logical query answering over Knowledge Graphs (KGs) is a fundamental yet complex task. A promising approach to achieve this is to embed queries and entities jointly into the same embedding space. Research along this line suggests that using multi-modal distribution to represent answer entities is more suitable than uni-modal distribution, as a single query may contain multiple disjoint answer subsets due to the compositional nature of multi-hop queries and the varying latent semantics of relations. However, existing methods based on multi-modal distribution roughly represent each subset without capturing its accurate cardinality, or even degenerate into uni-modal distribution learning during the reasoning process due to the lack of an effective similarity measure. To better model queries with diversified answers, we propose Query2GMM for answering logical queries over knowledge graphs. In Query2GMM, we present the GMM embedding to represent each query using a univariate Gaussian Mixture Model (GMM). Each subset of a query is encoded by its cardinality, semantic center and dispersion degree, allowing for precise representation of multiple subsets. Then we design specific neural networks for each operator to handle the inherent complexity that comes with multi-modal distribution while alleviating the cascading errors. Last, we design a new similarity measure to assess the relationships between an entity and a query's multi-answer subsets, enabling effective multi-modal distribution learning for reasoning. Comprehensive experimental results show that Query2GMM outperforms the best competitor by an absolute average of 6.35%.

Giant pre-trained code models (PCMs) start coming into the developers’ daily practices. Understanding the type and amount of software knowledge in PCMs is essential for integrating PCMs into software engineering (SE) tasks and unlocking their potential. In this work, we conduct the first systematic study on the SE factual knowledge in the state-of-the-art PCM CoPilot, focusing on APIs’ Fully Qualified Names (FQNs), the fundamental knowledge for effective code analysis, search and reuse. Driven by FQNs’ data distribution properties, we design a novel lightweight in-context learning on Copilot for FQN inference, which does not require code compilation as traditional methods or gradient update by recent FQN prompt-tuning. We systematically experiment with five in-context learning design factors to identify the best configuration for practical use. With this best configuration, we investigate the impact of example prompts and FQN data properties on CoPilot's FQN inference capability. Our results confirm that CoPilot stores diverse FQN knowledge and can be applied for FQN inference due to its high accuracy and non-reliance on code analysis. Additionally, our extended study shows that the in-context learning method can be generalized to retrieve other SE factual knowledge embedded in giant PCMs. Furthermore, we find that the advanced general model GPT-4 also stores substantial SE knowledge. Comparing FQN inference between CoPilot and GPT-4, we observe that as model capabilities improve, the same prompts yield better results. Based on our experience interacting with Copilot, we discuss various opportunities to improve human-CoPilot interaction in the FQN inference task.

The field of generating recommendations within the framework of causal inference has seen a recent surge.This approach enhances insights into the influence of recommendations on user behavior and helps in identifying the underlying factors. Existing research has often leveraged propensity scores to mitigate bias, albeit at the risk of introducing additional variance. Others have explored the use of unbiased data from randomized controlled trials, although this comes with assumptions that may prove challenging in practice. In this paper, we first present the causality-aware interpretation of recommendations and reveal how the underlying exposure mechanism can bias the maximum likelihood estimation (MLE) of observational feedback. Recognizing that confounders may be elusive, we propose a contrastive self-supervised learning to minimize exposure bias, employing inverse propensity scores and expanding the positive sample set. Building on this foundation, we present a novel contrastive counterfactual learning method (CCL) that incorporates three unique positive sampling strategies grounded in estimated exposure probability or random counterfactual samples. Through extensive experiments on two real-world datasets, we demonstrate that our CCL outperforms the state-of-the-art methods.

Recent studies have shown that recommender systems (RSs) are highly vulnerable to data poisoning attacks. Understanding attack tactics helps improve the robustness of RSs. We intend to develop efficient attack methods that use limited resources to generate high-quality fake user profiles to achieve 1) transferability among black-box RSs 2) and imperceptibility among detectors. In order to achieve these goals, we introduce textual reviews of products to enhance the generation quality of the profiles. Specifically, we propose a novel attack framework named R-Trojan, which formulates the attack objectives as an optimization problem and adopts a tailored transformer-based generative adversarial network (GAN) to solve it so that high-quality attack profiles can be produced. Comprehensive experiments on real-world datasets demonstrate that R-Trojan greatly outperforms state-of-the-art attack methods on various victim RSs under black-box settings and show its good imperceptibility.

As the deployment of artificial intelligence (AI) is changing many fields and industries, there are concerns about AI systems making decisions and recommendations without adequately considering various ethical aspects, such as accountability, reliability, transparency, explainability, contestability, privacy, and fairness. While many sets of AI ethics principles have been recently proposed that acknowledge these concerns, such principles are high-level and do not provide tangible advice on how to develop ethical and responsible AI systems. To gain insight on the possible implementation of the principles, we conducted an empirical investigation involving semi-structured interviews with a cohort of AI practitioners. The salient findings cover four aspects of AI system design and development, adapting processes used in software engineering: (i) high-level view, (ii) requirements engineering, (iii) design and implementation, (iv) deployment and operation.

With the widespread accumulation of observational data, researchers obtain a new direction to learn counterfactual effects in many domains (e.g., health care and computational advertising) without Randomized Controlled Trials (RCTs). However, observational data suffer from inherent missing counterfactual outcomes and distribution discrepancy between treatment and control groups due to behaviour preference. Motivated by recent advances in representation learning in domain adaptation, we propose a novel framework based on Cycle-Balanced REpresentation learning for counterfactual inference (CBRE) to solve the above problems. Specifically, we realize a robust and balanced representation for different groups using adversarial training. Meanwhile, we construct an information loop that preserves original data properties cyclically, reducing information loss when transforming data into latent representation space. Experimental results on three real-world datasets demonstrate that CBRE matches/outperforms the state-of-the-art methods, and it has a great potential to be applied to counterfactual inference.

Deep reinforcement learning enables an agent to capture users' interest through dynamic interactions with the environment. It uses a reward function to learn user's interest and to control the learning process, attracting great interest in recommendation research. However, most reward functions are manually designed; they are either too unrealistic or imprecise to reflect the variety, dimensionality, and non-linearity of the recommendation problem. This impedes the agent from learning an optimal policy in highly dynamic online recommendation scenarios. To address the above issue, we propose a generative inverse reinforcement learning approach that avoids the need of defining an elaborative reward function. In particular, we model the recommendation problem as an automatic policy learning problem. We first generate policies based on observed users' preferences and then evaluate the learned policy by a measurement based on a discriminative actor-critic network. We conduct experiments on an online platform, VirtualTB, and demonstrate the feasibility and effectiveness of our proposed approach via comparisons with several state-of-the-art methods.

Deep learning frameworks generally require sufficient training data to generalize well while fail to adapt on small or few-shot datasets. Meta-learning offers an effective means of tackling few-shot scenarios and has drawn increasing attention in recent years. Meta-optimization aims to learn a shared set of parameters across tasks for meta-learning while facing challenges in determining whether an initialization condition can be generalized to tasks with diverse distributions. In this regard, we propose a meta-gradient boosting framework that can fit diverse distributions based on a base learner (which learns shared information across tasks) and a series of gradient-boosted modules (which capture task-specific information). We evaluate the model on several few-shot learning benchmarks and demonstrate the effectiveness of our model in modulating task-specific meta-learned priors and handling diverse distributions.

A common challenge for most current recommender systems is the cold-start problem. Due to the lack of user-item interactions, the fine-tuned recommender systems are unable to handle situations with new users or new items. Recently, some works introduce the meta-optimization idea into the recommendation scenarios, i.e. predicting the user preference by only a few of past interacted items. The core idea is learning a global sharing initialization parameter for all users and then learning the local parameters for each user separately. However, most meta-learning based recommendation approaches adopt model-agnostic meta-learning for parameter initialization, where the global sharing parameter may lead the model into local optima for some users. In this paper, we design two memory matrices that can store task-specific memories and feature-specific memories. Specifically, the feature-specific memories are used to guide the model with personalized parameter initialization, while the task-specific memories are used to guide the model fast predicting the user preference. And we adopt a meta-optimization approach for optimizing the proposed method. We test the model on two widely used recommendation datasets and consider four cold-start situations. The experimental results show the effectiveness of the proposed methods.

Collaborative filtering (CF) has been successfully used to provide users with personalized products and services. However, dealing with the increasing sparseness of user-item matrix still remains a challenge. To tackle such issue, hybrid CF such as combining with content based filtering and leveraging side information of users and items has been extensively studied to enhance performance. However, most of these approaches depend on hand-crafted feature engineering, which is usually noise-prone and biased by different feature extraction and selection schemes. In this paper, we propose a new hybrid model by generalizing contractive auto-encoder paradigm into matrix factorization framework with good scalability and computational efficiency, which jointly models content information as representations of effectiveness and compactness, and leverage implicit user feedback to make accurate recommendations. Extensive experiments conducted over three large-scale real datasets indicate the proposed approach outperforms the compared methods for item recommendation.