The prevalence of mobile Internet techniques and Online-To-Offline (O2O) business models has led the emergence of various spatial crowdsourcing (SC) platforms in our daily life. A core issue of SC is to assign real-time tasks to suitable crowd workers. Existing approaches usually focus on the matching of two types of objects, tasks and workers, or assume the static offline scenarios, where the spatio-temporal information of all the tasks and workers is known in advance. Recently, some new emerging O2O applications incur new challenges: SC platforms need to assign three types of objects, tasks, workers and workplaces, and support dynamic real-time online scenarios, where the existing solutions cannot handle. In this paper, based on the aforementioned challenges, we formally define a novel dynamic online task assignment problem, called the trichromatic online matching in real-time spatial crowdsourcing (TOM) problem, which is proven to be NP-hard. Thus, we first devise an efficient greedy online algorithm. However, the greedy algorithm can be trapped into local optimal solutions easily. We then present a threshold-based randomized algorithm that not only guarantees a tighter competitive ratio but also includes an adaptive optimization technique, which can quickly learn the optimal threshold for the randomized algorithm. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.

Online event-based social networks (EBSNs) and studies on global event-participant arrangement strategies for EBSNs are becoming popular recently. Existing works measure satisfaction of an arrangement by a linear combination of few factors, weights of which are predefined and fixed, and do not allow users to provide feedbacks on whether accepting the arrangement or not. Besides, most of them only consider offline scenarios, where full information of users is known in advance. However, on real-world EBSN platforms, users can dynamically log in the platform and register for events on a first come, first served basis. In other words, online scenarios of event-participant arrangement strategies should be considered. In this work, we study a new event-participant arrangement strategy for online scenarios, the Feedback-Aware Social Event-participant Arrangement (FASEA) problem, where satisfaction scores of an arrangement are learned adaptively and users can choose to accept or reject the arranged events. Particularly, we model the problem as a contextual combinatorial bandit setting and use efficient and effective algorithms to solve the problem. The effectiveness and efficiency of the solutions are evaluated with extensive experimental studies and our findings indicate that the state-of-the-art Thompson Sampling that is reported to work well under basic multi-armed bandit does not perform well under FASEA.

With the rapid development of mobile internet and online to offline marketing model, various spatial crowdsourcing platforms, such as Gigwalk and Gmission, are getting popular. Most existing studies assume that spatial crowdsourced tasks are simple and trivial. However, many real crowdsourced tasks are complex and need to be collaboratively finished by a team of crowd workers with different skills. Therefore, an important issue of spatial crowdsourcing platforms is to recommend some suitable teams of crowd workers to satisfy the requirements of skills in a task. In this paper, to address the issue, we first propose a more practical problem, called Top-k team recommendation in spatial crowdsourcing (Top k TR) problem. We prove that the Top k TR problem is NP-hard and designs a two-level-based framework, which includes an approximation algorithm with provable approximation ratio and an exact algorithm with pruning techniques to address it. In addition, we study a variant of the Top k TR problem, called Top k TRL, where a team leader is appointed among each recommended team of crowd workers in order to coordinate different crowd workers conveniently, and the aforementioned framework can be extended to address this variant. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.

With the rapid development of smartphones, spatial crowdsourcing platforms are getting popular. A foundational research of spatial crowdsourcing is to allocate micro-tasks to suitable crowd workers. Most existing studies focus on offline scenarios, where all the spatiotemporal information of micro-tasks and crowd workers is given. However, they are impractical since micro-tasks and crowd workers in real applications appear dynamically and their spatiotemporal information cannot be known in advance. In this paper, to address the shortcomings of existing offline approaches, we first identify a more practical micro-task allocation problem, called the Global Online Micro-task Allocation in spatial crowdsourcing (GOMA) problem. We first extend the state-of-art algorithm for the online maximum weighted bipartite matching problem to the GOMA problem as the baseline algorithm. Although the baseline algorithm provides theoretical guarantee for the worst case, its average performance in practice is not good enough since the worst case happens with a very low probability in real world. Thus, we consider the average performance of online algorithms, a.k.a online random order model.We propose a two-phase-based framework, based on which we present the TGOA algorithm with 1 over 4 -competitive ratio under the online random order model. To improve its efficiency, we further design the TGOA-Greedy algorithm following the framework, which runs faster than the TGOA algorithm but has lower competitive ratio of 1 over 8. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.

Recently, with the development of mobile Internet and smartphones, the online minimum bipartite matching in real time spatial data (OMBM) problem becomes popular. Specifically, given a set of service providers with specific locations and a set of users who dynamically appear one by one, the OMBM problem is to find a maximum-cardinality matching with minimum total distance following that once a user appears, s/he must be immediately matched to an unmatched service provider, which cannot be revoked, before subsequent users arrive. To address this problem, existing studies mainly focus on analyzing the worst-case competitive ratios of the proposed online algorithms, but study on the performance of the algorithms in practice is absent. In this paper, we present a comprehensive experimental comparison of the representative algorithms of the OMBM problem. Particularly, we observe a surprising result that the simple and efficient greedy algorithm, which has been considered as the worst due to its exponential worst-case competitive ratio, is significantly more effective than other algorithms. We investigate the results and further show that the competitive ratio of the worst case of the greedy algorithm is actually just a constant, 3.195, in the average-case analysis. We try to clarify a 25-year misunderstanding towards the greedy algorithm and justify that the greedy algorithm is not bad at all. Finally, we provide a uniform implementation for all the algorithms of the OMBM problem and clarify their strengths and weaknesses, which can guide practitioners to select appropriate algorithms for various scenarios.

With the rapid development of Web 2.0 and Online To Offline (O2O) marketing model, variousonlineevent-basedsocialnetworks(EBSNs) are getting popular. An important task of EBSNs is to facilitate the most satisfactory event-participant arrangement for both sides, i.e., events enroll more participants and participants are arranged with personally interesting events. Existing approaches usually focus on the arrangement of each single event to a set of potential users, or ignore the conflicts between different events, which leads to infeasible or redundant arrangements. In this paper, to address the shortcomings of existing approaches, we first identify a more general and useful event-participant arrangement problem, calledGlobalEvent-participantArrangement withConflict andCapacity($GEACC$) problem, focusing on the conflicts of different events and making event-participant arrangements in a global view. We find that the GEACC problem is NP-hard due to the conflicts among events. Thus, we design two approximation algorithms with provable approximation ratios and an exact algorithm with pruning technique to address this problem. In addition, we propose an online setting of GEACC, called OnlineGEACC, which is also practical in real-world scenarios. We further design an online algorithm with provable performance guarantee. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.

With the rapid development of Web 2.0 and Online To Offline (O2O) marketing model, various online event-based social networks (EBSNs), such as Meetup and Whova, are getting popular. An important task of EBSNs is to facilitate the most satisfactory event-participant arrangement for both sides, i.e. events enroll more participants and participants are arranged with personally interesting events. Existing approaches usually focus on the arrangement of each single event to a set of potential users, and ignore the conflicts between different events, which leads to infeasible or redundant arrangements. In this paper, to address the shortcomings of existing approaches, we first identify a more general and useful event-participant arrangement problem, called Global Event-participant Arrangement with Conflict and Capacity (GEACC) problem, focusing on the conflicts of different events and making event-participant arrangements in a global view. Though it is useful, unfortunately, we find that the GEACC problem is NP-hard due to the conflict constraints among events. Thus, we design two approximation algorithms with provable approximation ratios and an exact algorithm with pruning technique to address this problem. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.

Online event-based social network (EBSN) platforms are becoming popular these days. An important task of managing EBSNs is to arrange proper social events to interested users. Existing approaches usually assume that each user only attends one event or ignore location information. The overall utility of such strategy is limited in real world: 1) each user may attend multiple events; 2) attending multiple events will incur spatio-temporal conflicts and travel expenses. Thus, a more intelligent EBSN platform that provides personalized event planning for each participant is desired. In this paper, we first formally define the problem of Utility-aware Social Event-participant Planning (USEP), which is proven to be NP-hard. To solve the USEP problem, we first devise a greedy-based heuristic algorithm, which performs fast under certain circumstances but has no approximation guarantee. We then present a two-step approximation framework, which not only guarantees a 1/2-approximation ratio but also includes a series of optimization techniques to improve its space/time efficiency. Finally, we verify the efficiency and effectiveness of the proposed methods through extensive experiments on real and synthetic datasets.

It is universal to see people obtain knowledge on micro-blog services by asking others decision making questions. In this paper, we study the Jury Selection Problem(JSP) by utilizing crowdsourcing for decision making tasks on micro-blog services. Specifically, the problem is to enroll a subset of crowd under a limited budget, whose aggregated wisdom via Majority Voting scheme has the lowest probability of drawing a wrong answer(Jury Error Rate-JER). Due to various individual error-rates of the crowd, the calculation of JER is non-trivial. Firstly, we explicitly state that JER is the probability when the number of wrong jurors is larger than half of the size of a jury. To avoid the exponentially increasing calculation of JER , we propose two efficient algorithms and an effective bounding technique. Furthermore, we study the Jury Selection Problem on two crowdsourcing models, one is for altruistic users( AltrM ) and the other is for incentive-requiring users( PayM ) who require extra payment when enrolled into a task. For the AltrM model, we prove the monotonicity of JER on individual error rate and propose an efficient exact algorithm for JSP. For the PayM model, we prove the NP-hardness of JSP on PayM and propose an efficient greedy-based heuristic algorithm. Finally, we conduct a series of experiments to investigate the traits of JSP, and validate the efficiency and effectiveness of our proposed algorithms on both synthetic and real micro-blog data.