Online recruitment platforms have revolutionized labor markets by enabling bidirectional engagement between job seekers and employers, but this transformation has also introduced complex decision-making challenges due to information overload and parallel decision processes. Existing research and algorithms often focus on static and one-way models, neglecting the dynamic feedback loops and preference adjustments inherent in two-way proactive recruitment. This study introduces ProMatch, a novel person-job matching approach designed to support decision-making for both sides. ProMatch formalizes recruitment as a multi-stage process involving intention formation, preference updates, and bilateral matching, capturing the sequential dependencies between decision outcomes. It also incorporates a dynamic preference learning mechanism grounded in self-regulation theory, which iteratively refines preferences using textual profiles, historical interactions, and feedback. Validation using a real-world IT enterprise dataset and a two-week field experiment demonstrates ProMatch’s effectiveness. Results show a 9% increase in click-through rates and a 20% improvement in interview-through rates, highlighting its ability to enhance prediction accuracy by dynamically modeling evolving preferences. ProMatch’s innovations offer actionable decision support for both job seekers and employers, ultimately improving recruitment efficiency and cost-effectiveness in modern recruitment ecosystems.

The proliferation and rapid spread of fake news on social media pose a significant threat to society, underscoring the urgent need for effective early detection methods. This paper introduces multimodal adversarial transfer learning (MATRAL), a novel approach designed for early fake news detection. MATRAL integrates multimodal learning with adversarial transfer learning. Through effective multimodal learning, MATRAL can form a comprehensive representation of news items on social media, including text, images, and publisher information. The adversarial transfer learning component enables MATRAL to dynamically adapt its knowledge to new domains, ensuring the approach’s ongoing relevance against the evolving fake news generation tactics. Using the MediaEval 15–16 data sets to simulate the early fake news detection scenario, we conduct extensive experiments to evaluate MATRAL’s performance against state-of-the-art methods in multimodal fake news detection, machine learning methods, and industrial practices. The experimental results conclusively demonstrate MATRAL’s superiority across various widely adopted metrics, showcasing its proficiency in early stage fake news detection. To further elucidate the contributions of various model components, a series of ablation studies are conducted. Furthermore, MATRAL’s interpretability and robustness are substantiated through additional experimental analyses. Our work introduces a novel and robust solution to the pressing challenge of multimodal fake news detection on social media, offering a significant contribution to the research and practice of responsible artificial intelligence. History: This paper has been accepted by Kaushik Dutta for the Special Issue on the Responsible AI and Data Science for Social Good. Funding: This research was partially supported by the National Natural Science Foundation of China [Grants 72495123, 72101007, and 72201061]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0514 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0514 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

The public sharing of user information opens the door for adversaries to infer private data, leading to privacy breaches and facilitating malicious activities. While numerous studies have concentrated on privacy leakage via public user attributes, the threats associated with the exposure of user relationships, particularly through network structure, are often neglected. This study aims to fill this critical gap by advancing the understanding and protection against privacy risks emanating from network structure, moving beyond direct connections with neighbors to include the broader implications of indirect network structural patterns. To achieve this, we first investigate the problem of Graph Privacy Leakage via Structure (GPS), and introduce a novel measure, the Generalized Homophily Ratio, to quantify the various mechanisms contributing to privacy breach risks in GPS. Based on this insight, we develop a novel graph private attribute inference attack, which acts as a pivotal tool for evaluating the potential for privacy leakage through network structures under worst-case scenarios. To protect users' private data from such vulnerabilities, we propose a graph data publishing method incorporating a learnable graph sampling technique, effectively transforming the original graph into a privacy-preserving version. Extensive experiments demonstrate that our attack model poses a significant threat to user privacy, and our graph data publishing method successfully achieves the optimal privacy-utility trade-off compared to baselines.

The abundance of multiple types of consumer digital footprints recorded on e-commerce platforms has fueled the design of personalized recommender systems for decision support. However, capturing consumers' inherent preferences for effective recommendations based on consumer digital footprints can be challenging because of the multitude of factors driving consumer behaviors. Model training and recommendation outcomes may become biased if other factors are inappropriately recognized as consumers' inherent preferences in the learning process. Drawing on consumer behavior theories, we tease out various factors that drive consumers' digital footprints at different consumption stages. We develop a novel recommendation approach, namely, DISC (Disentangling consumers' Inherent preferences, item Salience effect, and Conformity effect), which leverages disentangled representation learning with a causal graph to derive the effect of each factor driving consumer behaviors. This approach provides personalized and interpretable recommendations based on the inference of consumers' normative inherent preferences. The DISC model's identifiability is demonstrated through theoretical analysis, enabling rigorous causal inference based on observational data. To evaluate DISC's performance, extensive experiments are conducted on real-world data sets with a carefully designed protocol. The results reveal that DISC outperforms state-of-the-art baselines significantly and possesses good interpretability. Moreover, we illustrate the potential impact of different marketing strategies' by intervening on the disentangled causes through follow-up counterfactual analyses based on the causal graph. Our study contributes to the literature and practice by causally unpacking the behavioral mechanism behind consumers' digital footprints and designing an interpretable personalized recommendation approach anchored in their inherent preferences.

Deep learning methods on graph data have achieved remarkable efficacy across a variety of real-world applications, such as social network analysis and transaction risk detection. Nevertheless, recent studies have illuminated a concerning fact: even the most expressive Graph Neural Networks (GNNs) are vulnerable to graph adversarial attacks. While several methods have been proposed to enhance the robustness of GNN models against adversarial attacks, few have focused on a simple yet realistic approach: valuing the adversarial risks and focused safeguards at the node level. This empowers defenders to allocate heightened security level to vulnerable nodes, while lower to robust nodes. With this new perspective, we propose a novel graph defense strategy RisKeeper, such that the adversarial risk can be directly kept in the input graph. We start at valuing the adversarial risk, by introducing a cost-aware projected gradient descent attack that takes into account both cost avoidance and compliance with costs budgets. Subsequently, we present a learnable approach to ascertain the ideal security level for each individual node by solving a bi-level optimization problem. Through extensive experiments on four real-world datasets, we demonstrate that our method achieves superior performance surpassing state-of-the-art methods. Our in-depth case studies provide further insights into vulnerable and robust structural patterns, serving as inspiration for practitioners to exercise heightened vigilance.

The prosperity of mobile and financial technologies has bred and expanded various kinds of financial products to a broader scope of people, which contributes to financial inclusion. It brings non-trivial social benefits of diminishing financial inequality. However, the technical challenges in individual financial risk evaluation exacerbated by the unforeseen user characteristic distribution and limited credit history of new users, as well as the inexperience of newly-entered companies in handling complex data and obtaining accurate labels, impede further promotion of financial inclusion. To tackle these challenges, this paper develops a novel transfer learning algorithm (i.e., TransBoost) that combines the merits of tree-based models and kernel methods. The TransBoost is designed with a parallel tree structure and efficient weights updating mechanism with theoretical guarantee, which enables it to excel in tackling realworld data with high dimensional features and sparsity in O(n) time complexity. We conduct extensive experiments on two public datasets and a unique largescale dataset from Tencent Mobile Payment. The results show that the TransBoost outperforms other state-ofthe-art benchmark transfer learning algorithms in terms of prediction accuracy with superior efficiency, demonstrate stronger robustness to data sparsity, and provide meaningful model interpretation. Besides, given a financial risk level, the TransBoost enables financial service providers to serve the largest number of users including those who would otherwise be excluded by other algorithms. That is, the TransBoost improves financial inclusion.

随着数字经济时代的到来,数据作为一种重要的生产要素,深刻改变了管理决策范式.对具有超规模、跨领域、流信息的大数据的分析利用成为了赋能管理实践的重要因素,其中数据的质量与完备性是影响后续数据价值提炼的重要前提.然而受限于数据采集方式与过程、被采集主体行为模式特点等因素,数据常常呈现超高缺失率的特点.超高数据缺失会严重影响数据分析及所承载的管理决策效果.因而,预先对大数据进行有效完备化对保证后续分析决策效果具有重要意义.本文对大数据情境下的数据完备化问题进行了系统梳理,重点给出在超高维度、多源异构、时空关联的情境下的大数据完备化问题的主要挑战、求解思路及其对管理学研究的启示,以期为大数据完备化及赋能管理决策奠定理论和方法学基础.

Concerning the information overload of online reviews, this paper models a new review selection problem called the Informative Review Subset Selection problem (namely, IRSS) and demonstrates that it is NP-hard to solve and approximate. Furthermore, a novel heuristic method (namely, Combined Search-ComS) is proposed for seeking the solution to the problem and selecting a subset of reviews, which is consistent with the original review corpus in light of mutual information entropy. The proposed method is then comprehensively examined via extensive data experiments and a user study on Amazon data. Experimental results reveal the overall superiority of the proposed method in comparison with other extant methods of concern, showing that it is an effective way to select an informative subset of online reviews. The proposed method is deemed desirable and useful for online consumers and service providers.

In this era of the digital economy, proper management of digital privacy is critical for end-users, service providers, platform vendors, and the government. Addressing the increasing privacy concerns expressed by the public, research on digital privacy steadily grew over the recent decade, covering management, economics, and information science. Relevant regulation policies have also been adopted to control data privacy and the processing of personal data by digital service providers. As academic discussions accumulate, a convolution of conceptualizations of digital privacy emerges, which obstructs the interdisciplinary research progress. Based on a comprehensive review of related literature, this paper proposes an ontology of digital privacy and discusses emerging research themes. This paper emphasizes the interdisciplinary nature of privacy-related issues and provides the foundation for a merged view and practice-focused solutions.