Qi Zhang

While Graph Neural Networks (GNNs) have established themselves as the state-of-the-art paradigm in recommender systems, their performance is often hampered by severe data sparsity. Cross-domain recommendation (CDR) offers a promising solution by leveraging multi-domain interactions. However, existing methods typically rely on specialized, end-to-end architectures. This isolation prevents the reuse of mature single-domain GNNs. Moreover, these methods typically employ static or uniform transfer mechanisms, failing to capture the interest heterogeneity across domains and the personalized nature of cross-domain correlations. To bridge this gap, we propose GPCDR, a general Graph Prompting framework for Cross-Domain Recommendation. Designed as a plug-and-play solution, GPCDR transforms single-domain GNN-based recommenders into effective cross-domain recommenders via the graph prompting approach. Specifically, to handle interest heterogeneity across distinct graph structures, we introduce a multi-interest graph prompting module. It employs a Mixture-of-Experts (MoE) mechanism to generate adaptive, user-specific graph prompts, enabling the backbone to capture diverse interest patterns. Furthermore, to address the rigidity of interest alignment, we design a personalized domain similarity module. Instead of enforcing uniform constraints, this module learns user-specific affinity scores to dynamically gate knowledge transfer, accommodating users with varying degrees of cross-domain consistency. Extensive experiments on four real-world datasets demonstrate that GPCDR not only significantly boosts the performance of various GNN backbones but also consistently outperforms state-of-the-art specialized CDR baselines. Our code is available at https://github.com/liujinghong123/GPCDR.

Bundle construction aims to assemble sets of mutually compatible items that collectively satisfy user preferences. This problem naturally encompasses two interconnected tasks: bundle completion, which captures intra-bundle compatibility, and bundle generation, which constructs personalized bundles. However, existing approaches typically treat these tasks in isolation, neglecting their inherent synergy: completion models learn compatibility in entangled continuous spaces that are difficult to transfer, while generation models often suffer from sparse supervision and lack explicit compatibility constraints, leading to semantically incoherent bundles. To bridge this gap, we propose CoGen, a two-stage unified multi-modal framework for bundle construction. The core idea of CoGen is to transform compatibility-aware continuous representations learned from completion into discrete semantics to guide bundle generation. Specifically, CoGen first learns disentangled multi-modal item representations and performs bundle completion to capture intra-bundle compatibility. The learned representations are then discretized via residual quantization (RQ) to obtain explicit discrete semantics that make compatibility patterns reusable for generation. To further improve bundle generation quality, CoGen adopts a semantic-anchored non-autoregressive generator to generate bundles based on the learned discrete semantics. Extensive experiments on four public multi-modal datasets verify the effectiveness of CoGen compared to several state-of-the-art approaches. Our code is available at https://github.com/y2q109/CoGen.

Collaborative filtering is a critical technique in recommender systems. It has been increasingly viewed as a conditional generative task for user feedback data, where newly developed diffusion model shows great potential. However, existing studies on diffusion model lack effective solutions for modeling implicit feedback. Particularly, the standard isotropic diffusion process overlooks correlation between items, misaligned with the graphical structure of the interaction space. Meanwhile, Gaussian noise destroys personalized information in a user's interaction vector, causing difficulty in its reconstruction. In this paper, we adapt standard diffusion model and propose a novel Graph Signal Diffusion Model for Collaborative Filtering (named GiffCF). To better represent the correlated distribution of user-item interactions, we define a generalized diffusion process using heat equation on the item-item similarity graph. Our forward process smooths interaction signals with an advanced family of graph filters, introducing the graph adjacency as beneficial prior knowledge for recommendation. Our reverse process iteratively refines and sharpens latent signals in a noise-free manner, where the updates are conditioned on the user's history and computed from a carefully designed two-stage denoiser, leading to high-quality reconstruction. Finally, through extensive experiments, we show that GiffCF effectively leverages the advantages of both diffusion model and graph signal processing, and achieves state-of-the-art performance on three benchmark datasets.

Recent years have witnessed the rapid development of machine-learning-based intelligent recruitment services. Along this line, a large number of emerging models have been proposed, achieving remarkable performance in various tasks, such as person-job fit, job classification and salary prediction. However, existing studies are usually domain/task specific, which significantly hinders the adaptation of models for different industries/tasks with limited training data. To this end, in this paper, we propose a novel skill-aware prompt-based pretraining framework, namely RecruitPro, which is capable of learning unified representations on the recruitment data and adapting for various downstream tasks of intelligent recruitment services. To be specific, we first present a contextualized embedding model that is pretrained on a large-scale recruitment dataset. Then, we construct 13 downstream benchmark tasks that are representative in the recruitment process. Along this line, we propose a skill-aware prompt learning module to enhance the adaptability of the pretrained model on downstream tasks. This module includes a skill-related prompt, which is designed to explore key semantic information (i.e., skills) from recruitment text, and a task-related prompt, which is designed to bridge the gap between the pretrained model and different downstream tasks. Moreover, we propose a strategy for extracting potential skills to further improve the performance of our skill-aware prompt learning module. Finally, extensive experiments have clearly demonstrated the effectiveness of RecruitPro. In addition, a case study has been presented to discuss the privacy preserving issue of our RecruitPro.

The recent advances in smart building technologies have enabled us to collect massive Wi-Fi network based trajectory data, which provide an unparalleled opportunity for understanding the indoor user mobility pattern and enabling a wide range of business applications. While some previous studies have explored the Wi-Fi positioning of users, there still lacks a systematic and effective solution for indoor user mobility pattern analysis based on Wi-Fi trajectory data. To this end, in this paper, we propose a unified framework for modeling Wi-Fi trajectory data, namely HWTE, which can empower various tasks of indoor user mobility pattern analysis, such as user classification, next location prediction and schedule estimation. Specifically, we first propose a session trajectory construction module to extract the spatio-temporal semantic information from the Wi-Fi trajectories of users. Then, we devise a pre-training module to learn the unified representation of Wi-Fi trajectories. In particular, a session position embedding technique and a position query task is introduced to enhance the representation ability of the whole trajectory. Moreover, we further propose a hierarchical Transformer-based fine-tuning module to support various application tasks with time and space efficiency. Finally, we validate our framework on a real-world dataset with all three kinds of downstream tasks.

Self-supervised learning can be adopted to mine deep semantic information of visual data without a large number of human-annotated supervision by using a pretext task to pretrain a model. In this study, we proposed a novel self-supervised learning paradigm, namely multi-task self-supervised (MTSS) representation learning. Unlike existing self-supervised learning methods, which pretrain neural networks on the pretext task and then fine-tune the parameters of neural networks on the downstream task, in our scheme, downstream and pretext tasks are considered primary and auxiliary tasks, respectively, and are trained simultaneously. Our method involves maximizing the similarity of two augmented views of an image as an auxiliary task and using a multi-task network to train the primary task alongside the auxiliary task. We evaluated the proposed method on standard datasets and backbones through a rigorous experimental procedure. Experimental results revealed that proposed MTSS can achieve better performance and robustness than other self-supervised learning methods on multiple image classification data sets without using negative sample pairs and large batches. This simple yet effective method can inspire people to rethink self-supervised learning.

Online search engine has been widely regarded as the most convenient approach for information acquisition. Indeed, the intensive information-seeking behaviors of search engine users make it possible to exploit search engine queries as effective “crowd sensors” for event monitoring. While some researchers have investigated the feasibility of using search engine queries for coarse-grained event analysis, the capability of search engine queries for real-time event detection has been largely neglected. To this end, in this article, we introduce a large-scale and systematic study on exploiting real-time search engine queries for outbreak event detection, with a focus on earthquake rapid reporting. In particular, we propose a realistic system of real-time earthquake detection through monitoring millions of queries related to earthquakes from a dominant online search engine in China. Specifically, we first investigate a large set of queries for selecting the representative queries that are highly correlated with the outbreak of earthquakes. Then, based on the real-time streams of selected queries, we design a novel machine learning–enhanced two-stage burst detection approach for detecting earthquake events. Meanwhile, the location of an earthquake epicenter can be accurately estimated based on the spatial-temporal distribution of search engine queries. Finally, through the extensive comparison with earthquake catalogs from China Earthquake Networks Center, 2015, the detection precision of our system can achieve 87.9%, and the accuracy of location estimation (province level) is 95.7%. In particular, 50% of successfully detected results can be found within 62 s after earthquake, and 50% of successful locations can be found within 25.5 km of seismic epicenter. Our system also found more than 23.3% extra earthquakes that were felt by people but not publicly released, 12.1% earthquake-like special outbreaks, and meanwhile, revealed many interesting findings, such as the typical query patterns of earthquake rumor and regular memorial events. Based on these results, our system can timely feed back information to the search engine users according to various cases and accelerate the information release of felt earthquakes.

The value assessment of job skills is important for companies to select and retain the right talent. However, there are few quantitative ways available for this assessment. Therefore, we propose a data-driven solution to assess skill value from a market-oriented perspective. Specifically, we formulate the task of job skill value assessment as a Salary-Skill Value Composition Problem, where each job position is regarded as the composition of a set of required skills attached with the contextual information of jobs, and the job salary is assumed to be jointly influenced by the context-aware value of these skills. Then, we propose an enhanced neural network with cooperative structure, namely Salary-Skill Composition Network (SSCN), to separate the job skills and measure their value based on the massive job postings. Experiments show that SSCN can not only assign meaningful value to job skills, but also outperforms benchmark models for job salary prediction.

Given a set of objects V with a dissimilarity measure between pairs of objects in V, a PoCluster is a collection of sets P ⊂ powerset(V) partially ordered by the ⊂ relation such that S ⊂ T iff the maximal dissimilarity among objects in S is less than the maximal dissimilarity among objects in T. PoClusters capture categorizations of objects that are not strictly hierarchical, such as those found in ontologies. PoClusters can not, in general, be constructed using hierarchical clustering algorithms. In this paper, we examine the relationship between PoClusters and dissimilarity matrices and prove that PoClusters are in one-to-one correspondence with the set of dissimilarity matrices. The PoClustering problem is NP-Complete, and we present a heuristic algorithm for it in this paper. Experiments on both synthetic and real datasets demonstrate the quality and scalability of the algorithms.