Using crowdsourced data in location-based social networks to explore influence maximization

Cai, Zhipeng; Yan, Mingyuan; Li, Yingshu

doi:10.1109/infocom.2016.7524471

Cited by 97 publications

(29 citation statements)

References 21 publications

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“…To keep this property, we choose function I(x, I max ) = (I max − 1) 1 − (1 − x) 2 + 1 to measure the increase of influencing probability, where x is the input probability increasing parameter varying from one factor to another and I max is the maximum increase. Then we have I(0, I max ) = 1, I(1, I max ) = I max , ∂I(x,Imax) ∂x > 0 for x ∈ (0, 1).This function is borrowed from [31], in which it is used to measure the probability increase for attending certain social-network-based events.…”

Section: Mcs-specific Influence Propagation Modelsmentioning

confidence: 99%

“…There are multiple MCS-specific factors, and we illustrate how the input probability increasing parameter x in I(x, I max ) is defined in two exemplary factors: (a) whether the incentive is attractive; (b) whether the topic of a task is interesting. We choose these two because they are significant factors to influence the users participation willingness according to the state-of-the-art studies [30], [31].…”

Section: Mcs-specific Influence Propagation Modelsmentioning

confidence: 99%

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Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing

Wang

et al. 2019

IEEE Trans. on Mobile Comput.

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Worker recruitment is a crucial research problem in Mobile Crowd Sensing (MCS). While previous studies rely on a specified platform with a pre-assumed large user pool, this paper leverages the influence propagation on the social network to assist the MCS worker recruitment. We first select a subset of users on the social network as initial seeds and push MCS tasks to them. Then, influenced users who accept tasks are recruited as workers, and the ultimate goal is to maximize the coverage. Specifically, to select a near-optimal set of seeds, we propose two algorithms, named Basic-Selector and Fast-Selector, respectively. Basic-Selector adopts an iterative greedy process based on the predicted mobility, which has good performance but suffers from inefficiency concerns. To accelerate the selection, Fast-Selector is proposed, which is based on the interdependency of geographical positions among friends. Empirical studies on two real-world datasets verify that Fast-Selector achieves higher coverage than baseline methods under various settings, meanwhile, it is much more efficient than Basic-Selector while only sacrificing a slight fraction of the coverage. , software reuse, and online software development environment. He has published more than 50 papers in prestigious conferences and journals, such as ICWS, UbiComp, ICSP and etc. As a technical leader and manager, he has accomplished several key national projects on software engineering and smart cities. Cooperating with major smart-city solution providing companies, his research work has been adopted in more than 20 cities in China.

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Section: Mcs-specific Influence Propagation Modelsmentioning

confidence: 99%

Section: Mcs-specific Influence Propagation Modelsmentioning

confidence: 99%

Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing

Wang

et al. 2019

IEEE Trans. on Mobile Comput.

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“…With billions of users, location-based social networks (LBSNs), have become the most popular applications. In a LBSN, users can easily share their geospatial locations and location-based contents in the physical world [9,34]. The rich knowledge that has accumulated in these social networks enables a variety of location-based recommendations.…”

Section: (Communicated By Zhipeng Cai)mentioning

confidence: 99%

Exploring timeliness for accurate recommendation in location-based social networks

Xu¹,

Yang²,

Chu³

2018

Mathematical Foundations of Computing

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An individual's location history in the real world implies his or her interests and behaviors. This paper analyzes and understands the process of Collaborative Filtering (CF) approach, which mines an individual's preference from his/her geographic location histories and recommends locations based on the similarities between the user and others. We find that a CF-based recommendation process can be summarized as a sequence of multiplications between a transition matrix and visited-location matrix. The transition matrix is usually approximated by the user's interest matrix that reflect the similarity among users, regarding to their interest in visiting different locations. The visited-location matrix provides the history of visited locations of all users, which is currently available to the recommendation system. We find that recommendation results will converge if and only if the transition matrix remains unchanged; otherwise, the recommendations will be valid for only a certain period of time. Based on our analysis, a novel location-based accurate recommendation (LAR) method is proposed, which considers the semantic meaning and category information of locations, as well as the timeliness of recommending results, to make accurate recommendations. We evaluated the precision and recall rates of LAR, using a large-scale real-world data set collected from Brightkite. Evaluation results confirm that LAR offers more accurate recommendations, comparing to the state-of-art approaches.

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“…Mobile ad targeting research has been including location, terminal, content, and people . Because mobile devices such as smartphones become one of the essential items people will carry with almost at anywhere and anytime, advertising based on location and terminal attracts people's attention gradually . Moreover, some battery‐free devices will inevitablly be popular because of their highly accurate localization and tracking ability .…”

Section: Related Workmentioning

confidence: 99%

“…23 Because mobile devices such as smartphones become one of the essential items people will carry with almost at anywhere and anytime, advertising based on location and terminal attracts people's attention gradually. 24 Moreover, some battery-free devices will inevitablly be popular because of their highly accurate localization and tracking ability. 25,26 To this end, mobile advertising based on the content and people could improve the user's interest in advertising 27 thereby increasing the advertisement spread probability, which would naturally arise privacy issues.…”

Section: Mobile Advertisement Popularizationmentioning

confidence: 99%

Optimizing the interested area coverage with efficient mobile advertisement user selection

Yang

2017

Concurrency and Computation

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Summary Mobile advertisement distribution effects are vitally important for advertisers as well as users. Status quo studies are focusing on efficient distribution especially when user mobilities are involved. Unfortunately, previous studies have shown the interested area property during mobile advertisement propagation. In achieving efficient and effective mobile advertisement applications, this work advocates the concept of location‐centric mobile crowdsourcing network, where locations are vitally important for advertisement distribution, and mobile users need to be carefully selected for efficiency considerations. Different from traditional user‐centric and platform‐centric crowdsourcing networks, this work focuses on the mobile advertisement user selection problem when interested area coverage is considered. There are several fundamentally important challenges needed to be addressed before developing a location‐centric scheme for mobile advertisement user selection. First of all, we need to deal with the spatio‐temporal features for each user, where the interested area coverage ratio needs to be effectively evaluated. Even worse, budget constraint makes this problem intractable. In tackling aforementioned challenges, this work makes the following efforts: First, a budget‐constrained user selection problem is formulated when location sensitive mobile advertisement applications are considered, which is proved NP‐hard. Second, the submodularity feature is explored, and a simple but efficient heuristic algorithm is presented with guaranteed approximation ratio false(1−1efalse). Finally, extensive simulation results show that, our scheme could effectively improve the propagation effects for mobile advertisement with 125%. When considering the user's interest to different advertisements, the real user interest data set has also been used to validate that our proposed method could achieve improved performance than the random method.

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Using crowdsourced data in location-based social networks to explore influence maximization

Cited by 97 publications

References 21 publications

Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing

Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing

Exploring timeliness for accurate recommendation in location-based social networks

Optimizing the interested area coverage with efficient mobile advertisement user selection

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