Online Matching Research Articles

Online bilateral matching methodology for anti-epidemic resources based on spatial transmission risk

This paper focuses on the online anti-epidemic resource allocation between demanders and suppliers considering the epidemic spatial spread. The spatial crowdsourcing with sharing platform is an effective way for anti-epidemic resource allocation, and a reasonable online matching strategy can improve the efficiency of resource utilization. The paper proposes online matching heuristic strategy (HSTRF-LRLUF strategy) and designs an online batch bilateral matching algorithm for anti-epidemic resources, which considers the impact of grid spatial aggregation and diffusion risk of emerging infectious diseases. The population distribution within grids and the commuting patterns between grids can provide decision support for selecting online matching strategies of anti-epidemic resources. A larger matching time window focusing on the spatial transmission risk (TR) of the epidemic can obtain better matching results. However, with a smaller matching time window, the decision makers can focus on spatial agglomeration risk (OR) or spatial diffusion risk (IR). The paper effectively combines the spatial crowdsourcing model with the anti-epidemic resource allocation to achieve the allocation of emergency resources to individuals. A combined anti-epidemic resource online matching heuristic strategies is designed from the spatial agglomeration risk and the spatial diffusion risk. Decision makers can dynamically adjust the online matching strategies of anti-epidemic resources by evaluating the spatial agglomeration risk, the spatial diffusion risk, and the overall spatial transmission risk based on the real-time spread of the epidemic and the supply of anti-epidemic resources.

UWB-Assisted Bluetooth Localization Using Regression Models and Multi-Scan Processing.

Bluetooth devices have been widely used for pedestrian positioning and navigation in complex indoor scenes. Bluetooth beacons are scattered throughout the entire indoor walkable area containing stairwells, and pedestrian positioning can be obtained by the received Bluetooth packets. However, the positioning performance is sharply deteriorated by the multipath effects originating from indoor clutter and walls. In this work, an ultra-wideband (UWB)-assisted Bluetooth acquisition of signal strength value method is proposed for the construction of a Bluetooth fingerprint library, and a multi-frame fusion particle filtering approach is proposed for indoor pedestrian localization for online matching. First, a polynomial regression model is developed to fit the relationship between signal strength and location. Then, particle filtering is utilized to continuously update the hypothetical location and combine the data from multiple frames before and after to attenuate the interference generated by the multipath. Finally, the position corresponding to the maximum likelihood probability of the multi-frame signal is used to obtain a more accurate position estimation with an average error as low as 70 cm.

Evaluating video feedback to improve eSports performance in Street Fighter V

AbstracteSports is a newly rising field with rapidly increasing cultural validity. Despite the wide array of similarities between eSports and traditional sports, behavioral skill acquisition interventions have yet to be applied to video game skills. Although video feedback has been established as effective for traditional sports, further research is needed to validate its efficacy as a standalone intervention. In this study, the effects of video feedback were analyzed with three participants in a nonconcurrent multiple baseline across subjects design. Participants performed a specific in‐game behavior (an anti‐air attack) in a controlled match against the researcher, then viewed recordings of their performance and received positive and corrective feedback from the researchers. The generalization of the skill improvement was evaluated through the rate of successful skill implementation in ranked online matches. Results showed that the video feedback intervention improved performance over baseline for all participants and generalized to online matches.

A UWB indoor positioning method based on triple filtering fingerprint matching algorithm

Abstract In recent years, ultra-wideband (UWB) has gradually become a research hot spot in the field of indoor positioning because of its various advantages. Although UWB has such excellent performance in normal environments, the non-line-of-sight propagation of signals in complex indoor environments and the multi-path effect caused by obstacles will affect its positioning accuracy. To solve this problem, we use the fingerprint positioning method and optimize the previously commonly used k-nearest neighbor algorithm in the online matching phase. In this paper, we proposed a triple-filtered k-nearest neighbor algorithm based on sample distance weighting (TFWKNN). Experimental results show that the mean calculation error of TFWKNN is 63.4% less than the k-nearest algorithm, and the proposed algorithm has better prediction stability than other commonly used machine learning regression algorithms.

Class fairness in online matching

We initiate the study of fairness among classes of agents in online bipartite matching where there is a given set of offline vertices (aka agents) and another set of vertices (aka items) that arrive online and must be matched irrevocably upon arrival. In this setting, agents are partitioned into classes and the matching is required to be fair with respect to the classes. We adapt popular fairness notions (e.g. envy-freeness, proportionality, and maximin share) and their relaxations to this setting and study deterministic algorithms for matching indivisible items (leading to integral matchings) and for matching divisible items (leading to fractional matchings). For matching indivisible items, we propose an adaptive-priority-based algorithm, Match-and-Shift, prove that it achieves 12-approximation of both class envy-freeness up to one item and class maximin share fairness, and show that each guarantee is tight. For matching divisible items, we design a water-filling-based algorithm, Equal-Filling, that achieves (1−1e)-approximation of class envy-freeness and class proportionality; we prove 1−1e to be tight for class proportionality and establish a 34 upper bound on class envy-freeness. Finally, we discuss several challenges in designing randomized algorithms that achieve reasonable fairness approximation ratios. Nonetheless, we build upon Equal-Filling to design a randomized algorithm for matching indivisible items, Equal-Filling-OCS, which achieves 0.593-approximation of class proportionality.

Optimal analysis for bandit learning in matching markets with serial dictatorship

The problem of two-sided matching markets is well-studied in computer science and economics, owing to its diverse applications across numerous domains. Since market participants are usually uncertain about their preferences in various online matching platforms, an emerging line of research is dedicated to the online setting where one-side participants (players) learn their unknown preferences through multiple rounds of interactions with the other side (arms). Sankararaman et al. [23] provide an Ω(Nlog⁡(T)Δ2+Klog⁡(T)Δ) regret lower bound for this problem under serial dictatorship assumption, where N is the number of players, K(≥N) is the number of arms, Δ is the minimum reward gap across players and arms, and T is the time horizon. Serial dictatorship assumes arms have the same preferences, which is common in reality when one side participants have a unified evaluation standard. Recently, the work of Kong and Li [10] proposes the ET-GS algorithm and achieves an O(Klog⁡(T)Δ2) regret upper bound, which is the best upper bound attained so far. Nonetheless, a gap between the lower and upper bounds, ranging from N to K, persists. It remains unclear whether the lower bound or the upper bound needs to be improved. In this paper, we propose a multi-level successive selection algorithm that obtains an O(Nlog⁡(T)Δ2+Klog⁡(T)Δ) regret bound when the market satisfies serial dictatorship. To the best of our knowledge, we are the first to propose an algorithm that matches the lower bound in the problem of matching markets with bandits.

Online Matching: A Brief Survey

Matching, capturing allocation of items to unit-demand buyers, or tasks to workers, or pairs of collaborators, is a central problem in economics. Indeed, the growing prevalence of matching-based markets, many of which online in nature, has motivated much research in economics, operations research, computer science, and their intersection. This brief survey is meant as an introduction to the area of online matching, with an emphasis on recent trends, both technical and conceptual.

Pigeonhole Design: Balancing Sequential Experiments from an Online Matching Perspective

Practitioners and academics have long appreciated the benefits of covariate balancing when they conduct randomized experiments. For web-facing firms running online A/B tests, however, it still remains challenging in balancing covariate information when experimental subjects arrive sequentially. In this paper, we study an online experimental design problem, which we refer to as the online blocking problem. In this problem, experimental subjects with heterogeneous covariate information arrive sequentially and must be immediately assigned into either the control or the treated group. The objective is to minimize the total discrepancy, which is defined as the minimum weight perfect matching between the two groups. To solve this problem, we propose a randomized design of experiment, which we refer to as the pigeonhole design. The pigeonhole design first partitions the covariate space into smaller spaces, which we refer to as pigeonholes, and then, when the experimental subjects arrive at each pigeonhole, balances the number of control and treated subjects for each pigeonhole. We analyze the theoretical performance of the pigeonhole design and show its effectiveness by comparing against two well-known benchmark designs: the matched-pair design and the completely randomized design. We identify scenarios when the pigeonhole design demonstrates more benefits over the benchmark design. To conclude, we conduct extensive simulations using Yahoo! data to show a 10.2% reduction in variance if we use the pigeonhole design to estimate the average treatment effect. This paper was accepted by George Shanthikumar, data science. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.02184 .

Adwords with Unknown Budgets and Beyond

In the classic Adwords problem introduced by [Mehta A, Saberi A, Vazirani U, Vazirani V (2007) Adwords and generalized online matching. J. ACM 54(5):22-es.], we have a bipartite graph between advertisers and queries. Each advertiser has a maximum budget that is known a priori. Queries are unknown a priori and arrive sequentially. When a query arrives, advertisers make bids, and we (immediately and irrevocably) decide which (if any) Ad to display based on the bids and advertiser budgets. The winning advertiser for each query pays their bid up to their remaining budget. Our goal is to maximize total budget used without any foreknowledge of the arrival sequence (which could be adversarial). We consider the setting where the online algorithm does not know the advertisers’ budgets a priori and the budget of an advertiser is revealed to the algorithm only when it is exceeded. A naïve greedy algorithm is 0.5 competitive for this setting, and finding an algorithm with better performance remained an open problem. We show that no deterministic algorithm has competitive ratio better than 0.5 and give the first (randomized) algorithm with strictly better performance guarantee. We show that the competitive ratio of our algorithm is at least 0.522 but also strictly less than [Formula: see text]. We present novel applications of budget oblivious algorithms in search ads and beyond. In particular, we show that our algorithm achieves the best possible performance guarantee for deterministic online matching in the presence of multichannel traffic [Manshadi V, Rodilitz S, Saban D, Suresh A (2022) Online algorithms for matching platforms with multi-channel traffic. Proc. 23rd ACMConf. Econom. Comput. (ACM, NewYork), 986–987.]. This paper was accepted by Omar Besbes, revenue management and market analytics. Funding: NSF Division of Civil, Mechanical, and Manufacturing Innovation [Grant 2340306], and Google Research Scholar Program. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2021.03243 .

Signaling Effects Under Dynamic Capacity in Online Matching Platforms: Evidence from Online Health Consultation Communities

Online Health Consultation Communities (OHCCs) have emerged as vital platforms connecting patients with physicians for online consultations. However, finding the right match between patients and physicians can be tricky due to physicians’ changing capacity to consult on the OHCC. Our study delves into how signals provided by OHCCs on physicians’ professional status and behaviors can help make these matches successful, especially when considering their capacity fluctuations. We differentiate between two types of signals pertaining to physicians–owned, pertaining to their professional status, and earned, pertaining to their OHCC activity and patient reviews of the physician. Employing a Hidden Markov Model to analyze data from a large OHCC on physicians’ voluntary online consultations with patients, we find the role of the signals in efficient matching to be contingent on the capacity state of the physician. Physicians’ professional status is particularly important when they have less time available, and showing active participation in the community can make the status even more impactful. Conversely, when physicians have more availability, patient feedback becomes crucial, even diminish the importance of professional status. These insights suggest that OHCCs should tailor how information on physicians’ professional status and patient feedback are presented depending on physicians’ availability as this can help patients to make better choices. By being active in the OHCC and earning favorable patient feedback, physicians with more availability can improve their attractiveness to patients, even offsetting concerns that can stem from the lack of seniority of the physician. The findings underscore the need for OHCCs to develop signaling and matching mechanisms that consider the capacity of physicians, thereby fostering efficient and satisfactory patient-physician consultations.

Interactive Theorem Provers: Applications in AI, Opportunities, and Challenges

Interactive theorem provers (ITPs) are computer programs in which axioms and a conjecture are stated in a formal language, and a user provides the ITP with relatively high-level steps of a formal proof for the conjecture. Then, by invoking automated theorem provers, the ITP tries to generate low-level steps that fill the gaps between the steps provided by the user, thus forming a complete formal proof of the conjecture. The ITP also checks the entire formal proof against the axioms, thus confirming the soundness of all derivations in the formal proof. In this talk, I will discuss the existing opportunities and potential benefits to applying ITPs to reason about and verify AI concepts, algorithms, and software. I will also discuss the challenges we have to being able to apply ITPs in AI and reap those benefits. I will do so by discussing a number of my previous projects on the application of ITPs to different AI concepts, algorithms, and software systems. These projects span different areas of planning (classical planning, temporal planning, and planning under uncertainty) as well as algorithms with applications in algorithmic game theory, like general graph matching and online matching.

Price of Anarchy in Algorithmic Matching of Romantic Partners

Algorithmic matching is a pervasive mechanism in our social lives and is becoming a major medium through which people find romantic partners and potential spouses. However, romantic matching markets pose a principal-agent problem with the potential for moral hazard. The agent’s (or system’s) interest is to maximize the use of the matching website, while the principal’s (or user’s) interest is to find the best possible match. This creates a conflict of interest: the optimal matching of users may not be aligned with the platform’s goal of maximizing engagement, as it could lead to long-term relationships and fewer users using the site over time. Here, we borrow the notion of price of anarchy from game theory to quantify the decrease in social efficiency of online algorithmic matching sites where engagement is in tension with user utility. We derive theoretical bounds on the price of anarchy and show that it can be bounded by a constant that does not depend on the number of users in the system. This suggests that as online matching sites grow, their potential benefits scale up without sacrificing social efficiency. Further, we conducted experiments with human subjects in a matching market and compared the social welfare achieved by an optimal matching service against a self-interested matching algorithm. We show that introducing competition among matching sites aligns the self-interested behavior of platform designers with their users and increases social efficiency.

Optimization of Truck–Cargo Online Matching for the Less-Than-Truck-Load Logistics Hub under Real-Time Demand

Reasonable matching of capacity resources and transported cargoes is the key to realizing intelligent scheduling of less-than-truck-load (LTL) logistics. In practice, there are many types and numbers of participating objects involved in LTL logistics, such as customers, orders, trucks, unitized implements, etc. This results in a complex and large number of matching schemes where truck assignments interact with customer order service sequencing. For the truck–cargo online matching problem under real-time demand, it is necessary to comprehensively consider the online matching process of multi-node orders and the scheduling of multi-types of trucks. Combined with the actual operation scenario, a mixed-integer nonlinear programming model is introduced, and an online matching algorithm with a double-layer nested time window is designed to solve it. By solving the model in a small numerical case using Gurobi and the online matching algorithm, the validity of the model and the effectiveness of the algorithm are verified. The results indicate that the online matching algorithm can obtain optimization results with a lower gap while outperforming in terms of computation time. Relying on the realistic large-scale case for empirical analysis, the optimization results in a significant reduction in the number of trips for smaller types of trucks, and the average truck loading efficiency has reached close to 95%. The experimental results demonstrate the general applicability and effectiveness of the algorithm. Thus, it helps to realize the on-demand allocation of capacity resources and the timely response of transportation scheduling of LTL logistics hubs.

Event-driven based simulation method for security and stability control strategies of power system

Since the security and stability control strategies (SSCSs) play a pivotal role in maintaining system stability, devising the strategies accurately and reliably is of great significance. Currently, SSCSs are typically formulated according to the ‘offline simulation and online matching’ approach. However, existing commercial simulation software is difficult to simulate the dynamic triggering mechanisms and intricate control logic of SSCSs. To simulate the set of SSCSs systematically and comprehensively, this paper has proposed an event-driven approach for simulations. Firstly, the SSCS is modeled as an event-driven model containing three elements: event, event listening, and event processing. Then, on the basis of the event triggering mechanism presented in this paper, the complex control logics of SSCSs, such as the prioritization of tripping generators, are implemented. Through event-driven models of SSCSs and subsequent utilization of the event-triggered mechanism, independent and dynamic responses of SSCSs can be realized, thus avoiding interference in the simulation of multiple SSCSs. Finally, the effectiveness of the proposed method is verified based on the IEEE 39-bus system.

Truthful Matching with Online Items and Offline Agents

We study truthful mechanisms for welfare maximization in online bipartite matching. In our (multi-parameter) setting, every buyer is associated with a (possibly private) desired set of items, and has a private value for being assigned an item in her desired set. Unlike most online matching settings, where agents arrive online, in our setting the items arrive one by one in an adversarial order while the buyers are present for the entire duration of the process. This poses a significant challenge to the design of truthful mechanisms, due to the ability of buyers to strategize over future rounds. We provide an almost full picture of the competitive ratios in different scenarios, including myopic vs. non-myopic agents, tardy vs. prompt payments, and private vs. public desired sets. Among other results, we identify the frontier up to which the celebrated e/(e-1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$e/(e-1)$$\\end{document} competitive ratio for the vertex-weighted online matching of Karp, Vazirani and Vazirani extends to truthful agents and online items.

Compatibility and Information Asymmetry in Online Matching Platforms

Firms seeking business partners and individuals seeking life partners face several challenges in addition to finding available candidates. One of these challenges is uncertainty about a candidate’s compatibility with the match-seeker on various subjective criteria. Another challenge is authentication of objective quality features, particularly in online settings where credentials can be easily misrepresented. Online matching platforms can address these challenges through counseling services that help assess mutual compatibility of match-seekers and authentication services to validate match-seekers’ credentials. Using a stylized model, we show that the platform’s decisions to offer counseling and authentication services are influenced by match-seeker behavior in response to factors such as the platform’s counseling capability, the composition of the matching market, and the interactions between the two services. This leads to optimal strategies for the platform that are not obvious and can even be counterintuitive. For instance, offering better counseling does not necessarily result in higher revenues for the platform. And it may be optimal for the platform to reduce its access fee when it decides to offer an authentication service. We also show that when the platform’s counseling capability is modest, an increase in this capability increases the value of authentication (i.e., counseling complements authentication); however, when the platform’s counseling capability is high, counseling and authentication become substitutes, because narrowing the search to only high-value candidates based on authentication can exclude some compatible candidates. Our analysis and results provide valuable insights for platform operators on the relative value of counseling and authentication, and for researchers studying matching platforms for vertically and horizontally differentiated markets. This paper was accepted by Hemant Bhargava, information systems. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.03807 .

Design and Analysis of Stochastic Processing and Matching Networks

Stochastic Processing Networks (SPNs) [24] are ubiquitous in engineering with applications in Data Centers (eg. packet routing), Telecommunication (eg. call centers), Transportation, etc. Stochastic Matching Networks (SMNs) are newly emerging variation of SPNs where 'jobs' and 'servers' arrive in the system and are matched using a decision rule. SMNs have applications in Online matching markets (eg. uber platform), Healthcare (eg. kidney transplant), Quantum Networks, Assemble-to-order (eg. manufacturing plants) and many other crucial problems. The aim of my research is to design provably efficient decision-making policies for Stochastic Processing Networks (SPNs) and Stochastic Matching Networks (SMNs) and develop analytical tools to obtain performance guarantees on their throughput and delay.

Online Matching Frameworks Under Stochastic Rewards, Product Ranking, and Unknown Patience

Ranking Products for Customers with Unknown Patience In e-commerce, customers have an unknown patience in terms of how far down the page they are willing to scroll. In light of this, how should products be ranked? The e-commerce retailer’s problem is further complicated by the fact that the supply of each product may be limited, and that multiple customers who are interested in these products will arrive over time. In “Online Matching Frameworks Under Stochastic Rewards, Product Ranking, and Unknown Patience,” Brubach, Grammel, Ma, and Srinivasan provide a general framework for studying this complicated problem that decouples the product ranking problem for a single customer from the online matching of products to multiple customers over time. They also develop a better algorithm for the single-customer product ranking problem under well-studied cascade-click models. Finally, they introduce a model where the products are also arriving over time and cannot be included in the search rankings until they arrive.

The determinants of online matching platforms for freight services

With the development of information technology and the success of E-commerce, online freight exchange platforms have expanded to trucking freight services by matching truckers with shippers directly. Despite the endeavors of online freight platforms to emulate the success of other platform services, they face difficulties in facilitating successful matches. By analyzing the transactional data of an existing freight service platform, we seek to identify the salient factors that improve platform matching rates using a linear regression with splines for freight rate, capturing non-linearities, and a RUM discrete outcome model for matching. Both models are estimated jointly (simultaneously) using full information maximum likelihood (FIML). The empirical findings indicate that successful matching is more likely if the online freight platforms allow making substantial price adjustments once a job has been posted, as well as allowing shippers to have a longer lead time for loading their shipments, particularly in cases where finding a suitable trucker is challenging. Furthermore, allowing customers to revise shipment information provides greater flexibility in accommodating customer needs and ultimately enhances the likelihood of successful matching. Our analysis provides actionable and pragmatic suggestions to improve matching rates for online platforms of freight services.

POMF: A Privacy-preserved On-chain Matching Framework

With the growth of the Internet, an increasing number of online matching platforms have emerged. However, current online matching platforms need to address three open challenges to improve user experience: (1) transparency and inherent issues with centralized platforms; (2) privacy concerns of users’ sensitive information; and (3) high service fees for matching services. In this paper, we propose a Privacy-preserved On-chain Matching Framework (POMF) that is suitable for a broad range of matching scenarios to overcome the challenges above simultaneously. In order to address the first challenge, POMF is constructed via the decentralized blockchain, based on which smart contracts are utilized to execute matching services automatically and publicly. To address the second challenge, we combine Order-Preserved Encryption (OPE) with Software Guard Extensions (SGX) to design a privacy-preserved on-chain matching mechanism that can enable information matching among different users in the encrypted domain, while revealing only order information and maintaining confidentiality. To address the third challenge, we optimize the OPE algorithm to reduce its computing and communication overhead, which further reduces the matching service fee. Besides, we ensure the fairness of POMF by designing a reasonable price mechanism that prices the contact information based on the matching degree between the involved matched users. Finally, we prove the availability of POMF via an comprehensive analysis and demonstrate the practicality of POMF by implementing a prototype and deploying it on a Fabric test network with extensive simulations.