arXiv subjects

Hongjia Wu

Publications and source records attributed to Hongjia Wu.

7 recordsLinked to original sources

arXiv2026

CritLens: Visual Analytics for Criteria Discovery in Review-Based Decision Making

We present CritLens, a visual analytics system that helps users build personalized multi-criteria decision models from review text. In everyday decisions -- choosing equipment, hotels, or restaurants -- evaluation criteria are either preset by platforms or generated by LLMs, leaving users unable to discover, adjust, or verify them against the underlying evidence. This is problematic because many preferences are latent: they surface only upon encountering specific reviews, and any fixed framework risks overlooking low-frequency but decisive details. CritLens addresses this gap by using LLMs to transform reviews into an initial AHP decision model, then supporting iterative, human-in-the-loop refinement. Through coverage gap detection in the embedding space, users discover criteria missed by the initial model; through interactive weight adjustment under AHP consistency constraints, they express personal priorities; and through a multi-level scorecard and exportable decision report, they trace every ranking back to the original review text. Two case studies, an eight-participant user study, and a quantitative consistency-repair experiment demonstrate the system's effectiveness.

cs.HC↗

arXiv2026

Doc2AHP: Inferring Structured Multi-Criteria Decision Models via Semantic Trees with LLMs

While Large Language Models (LLMs) demonstrate remarkable proficiency in semantic understanding, they often struggle to ensure structural consistency and reasoning reliability in complex decision-making tasks that demand rigorous logic. Although classical decision theories, such as the Analytic Hierarchy Process (AHP), offer systematic rational frameworks, their construction relies heavily on labor-intensive domain expertise, creating an "expert bottleneck" that hinders scalability in general scenarios. To bridge the gap between the generalization capabilities of LLMs and the rigor of decision theory, we propose Doc2AHP, a novel structured inference framework guided by AHP principles. Eliminating the need for extensive annotated data or manual intervention, our approach leverages the structural principles of AHP as constraints to direct the LLM in a constrained search within the unstructured document space, thereby enforcing the logical entailment between parent and child nodes. Furthermore, we introduce a multi-agent weighting mechanism coupled with an adaptive consistency optimization strategy to ensure the numerical consistency of weight allocation. Empirical results demonstrate that Doc2AHP not only empowers non-expert users to construct high-quality decision models from scratch but also significantly outperforms direct generative baselines in both logical completeness and downstream task accuracy.

cs.AI↗

arXiv2025

MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services

Timely updating of Internet of Things data is crucial for achieving immersion in vehicular metaverse services. However, challenges such as latency caused by massive data transmissions, privacy risks associated with user data, and computational burdens on metaverse service providers (MSPs) hinder the continuous collection of high-quality data. To address these challenges, we propose an immersion-aware model trading framework that enables efficient and privacy-preserving data provisioning through federated learning (FL). Specifically, we first develop a novel multi-dimensional evaluation metric for the immersion of models (IoM). The metric considers the freshness and accuracy of the local model, and the amount and potential value of raw training data. Building on the IoM, we design an incentive mechanism to encourage metaverse users (MUs) to participate in FL by providing local updates to MSPs under resource constraints. The trading interactions between MSPs and MUs are modeled as an equilibrium problem with equilibrium constraints (EPEC) to analyze and balance their costs and gains, where MSPs as leaders determine rewards, while MUs as followers optimize resource allocation. To ensure privacy and adapt to dynamic network conditions, we develop a distributed dynamic reward algorithm based on deep reinforcement learning, without acquiring any private information from MUs and other MSPs. Experimental results show that the proposed framework outperforms state-of-the-art benchmarks, achieving improvements in IoM of 38.3% and 37.2%, and reductions in training time to reach the target accuracy of 43.5% and 49.8%, on average, for the MNIST and GTSRB datasets, respectively. These findings validate the effectiveness of our approach in incentivizing MUs to contribute high-value local models to MSPs, providing a flexible and adaptive scheme for data provisioning in vehicular metaverse services.

cs.LG↗

arXiv2025

A QoE-Driven Personalized Incentive Mechanism Design for AIGC Services in Resource-Constrained Edge Networks

With rapid advancements in large language models (LLMs), AI-generated content (AIGC) has emerged as a key driver of technological innovation and economic transformation. Personalizing AIGC services to meet individual user demands is essential but challenging for AIGC service providers (ASPs) due to the subjective and complex demands of mobile users (MUs), as well as the computational and communication resource constraints faced by ASPs. To tackle these challenges, we first develop a novel multi-dimensional quality-of-experience (QoE) metric. This metric comprehensively evaluates AIGC services by integrating accuracy, token count, and timeliness. We focus on a mobile edge computing (MEC)-enabled AIGC network, consisting of multiple ASPs deploying differentiated AIGC models on edge servers and multiple MUs with heterogeneous QoE requirements requesting AIGC services from ASPs. To incentivize ASPs to provide personalized AIGC services under MEC resource constraints, we propose a QoE-driven incentive mechanism. We formulate the problem as an equilibrium problem with equilibrium constraints (EPEC), where MUs as leaders determine rewards, while ASPs as followers optimize resource allocation. To solve this, we develop a dual-perturbation reward optimization algorithm, reducing the implementation complexity of adaptive pricing. Experimental results demonstrate that our proposed mechanism achieves a reduction of approximately $64.9\%$ in average computational and communication overhead, while the average service cost for MUs and the resource consumption of ASPs decrease by $66.5\%$ and $76.8\%$, respectively, compared to state-of-the-art benchmarks.

cs.GT↗

arXiv2025

Retrieval Augmented Decision-Making: A Requirements-Driven, Multi-Criteria Framework for Structured Decision Support

Various industries have produced a large number of documents such as industrial plans, technical guidelines, and regulations that are structurally complex and content-wise fragmented. This poses significant challenges for experts and decision-makers in terms of retrieval and understanding. Although existing LLM-based Retrieval-Augmented Generation methods can provide context-related suggestions, they lack quantitative weighting and traceable reasoning paths, making it difficult to offer multi-level and transparent decision support. To address this issue, this paper proposes the RAD method, which integrates Multi-Criteria Decision Making with the semantic understanding capabilities of LLMs. The method automatically extracts key criteria from industry documents, builds a weighted hierarchical decision model, and generates structured reports under model guidance. The RAD framework introduces explicit weight assignment and reasoning chains in decision generation to ensure accuracy, completeness, and traceability. Experiments show that in various decision-making tasks, the decision reports generated by RAD significantly outperform existing methods in terms of detail, rationality, and structure, demonstrating its application value and potential in complex decision support scenarios.

cs.AI↗

arXiv2022

FALCON: Fast and Accurate Multipath Scheduling using Offline and Online Learning

Multipath transport protocols enable the concurrent use of different network paths, benefiting a fast and reliable data transmission. The scheduler of a multipath transport protocol determines how to distribute data packets over different paths. Existing multipath schedulers either conform to predefined policies or to online trained policies. The adoption of millimeter wave (mmWave) paths in 5th Generation (5G) networks and Wireless Local Area Networks (WLANs) introduces time-varying network conditions, under which the existing schedulers struggle to achieve fast and accurate adaptation. In this paper, we propose FALCON, a learning-based multipath scheduler that can adapt fast and accurately to time-varying network conditions. FALCON builds on the idea of meta-learning where offline learning is used to create a set of meta-models that represent coarse-grained network conditions, and online learning is used to bootstrap a specific model for the current fine-grained network conditions towards deriving the scheduling policy to deal with such conditions. Using trace-driven emulation experiments, we demonstrate FALCON outperforms the best state-of-the-art scheduler by up to 19.3% and 23.6% in static and mobile networks, respectively. Furthermore, we show FALCON is quite flexible to work with different types of applications such as bulk transfer and web services. Moreover, we observe FALCON has a much faster adaptation time compared to all the other learning-based schedulers, reaching almost an 8-fold speedup compared to the best of them. Finally, we have validated the emulation results in real-world settings illustrating that FALCON adapts well to the dynamicity of real networks, consistently outperforming all other schedulers.

cs.NI↗

arXiv2020

A Survey on Universal Design for Fitness Wearable Devices

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in personal mobile devices, from smartphones towards wearable devices. Wearable devices come in many different forms targeting different application scenarios. Among these, the fitness wearable devices (FWDs) are proven to be one of the forms that intrigue the market and occupy an increasing trend in terms of the market share. Nevertheless, although the fitness wearable devices nowadays are functionally self-contained based on the advanced sensor, computation, and communicative technologies, there is still a large gap to truly satisfy the target customer group, i.e., accessible to and usable by a larger quantity of users. This fuels the research area on applying the universal design principles to fitness wearable devices. In this survey, we first present the background of FWDs and show the acceptance and adaption challenges of the corresponding user groups. We then review the universal design principle and how it and its relative approaches could be used in FWDs. Further, we collect the available FWDs that bear the universal design principles in their development circles. Last, we open up the discussion based on the surveyed literature and provide the insight of potential future work.

cs.HC↗