Tang, Shaojie

Permanent URI for this collectionhttps://hdl.handle.net/10735.1/5142

Shaojie Tang is an Associate Professor of Information Systems. His current research interests include:

  • Mobile Commerce
  • Social Networks
  • Optimization
  • Game Theory


Recent Submissions

Now showing 1 - 3 of 3
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    Towards Near Optimal Wifi Offloading With Uncertain Contact Duration
    (Institute of Electrical and Electronics Engineers Inc.) Dong, C.; Li, Z.; Qu, Y.; Wu, Q.; Tang, Shaojie; Qin, Z.; 7882147727666764710007 (Tang, S); Tang, Shaojie
    Due to the simplicity of implementation, user initiated WiFi offloading becomes more and more popular, and naturally the benefits of users become the main optimization goal. We notice the inter-contact and intra-contact durations could be uncertain in reality by reason of the user mobility and network dynamics. The two uncertain durations can cause great impact on the benefit of users, however, they were either ignored or simply assumed to be deterministic in most previous works. In this paper, for the first time, we study WiFi offloading problem with uncertain contact durations. The aim is to guarantee the benefit of users (delay and payment) without damaging operator’s benefit (amount of the offloaded traffic) at the same time. We propose a MAB-based Online Offloading scheme (MABOO) to solve the problem and prove the near-optimality of MABOO in terms of the utility theoretically. Extensive simulations show that MABOO always approaches the optimal scheme, and achieves higher utility as well as offloads more traffic compared with the minimal payment and on-the-spot-offloading schemes.
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    On Designing Distributed Auction Mechanisms for Wireless Spectrum Allocation
    (Institute of Electrical and Electronics Engineers Inc.) Yang, S.; Peng, D.; Meng, T.; Wu, F.; Chen, G.; Tang, Shaojie; Li, Z.; Luo, T. T.; 7882147727666764710007 (Tang, S); Tang, Shaojie
    Auctions are believed to be effective methods to solve the problem of wireless spectrum allocation. Existing spectrum auction mechanisms are all centralized and suffer from several critical drawbacks of the centralized systems, which motivates the design of distributed spectrum auction mechanisms. However, extending a centralized spectrum auction to a distributed one broadens the strategy space of agents from one dimension (bid) to three dimensions (bid, communication, and computation), and thus cannot be solved by traditional approaches from mechanism design. In this paper, we propose two distributed spectrum auction mechanisms, namely distributed VCG and FAITH. Distributed VCG implements the celebrated Vickrey-Clarke-Groves mechanism in a distributed fashion to achieve optimal social welfare, at the cost of exponential communication overhead. In contrast, FAITH achieves sub-optimal social welfare with tractable computation and communication overhead. We prove that both of the two proposed mechanisms achieve faithfulness, i.e., the agents' individual utilities are maximized, if they follow the intended strategies. Besides, we extend FAITH to adapt to dynamic scenarios where agents can arrive or depart at any time, without violating the property of faithfulness. We implement distributed VCG and FAITH, and evaluate their performance in various setups. ©2018 IEEE
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    Discount Allocation for Revenue Maximization in Online Social Networks
    (Association for Computing Machinery) Han, K.; Xu, C.; Gui, F.; Tang, Shaojie; Huang, H.; Luo, J.; Tang, Shaojie
    Viral marketing through online social networks (OSNs) has aroused great interests in the literature. However, the fundamental problem of how to optimize the "pure gravy" of a marketing strategy through influence propagation in OSNs still remains largely open. In this paper, we consider a practical setting where the "seed nodes" in an OSN can only be probabilistically activated by the product discounts allocated to them, and make the first attempt to seek a discount allocation strategy to maximize the expected difference of profit and cost (i.e., revenue) of the strategy. We show that our problem is much harder than the conventional influence maximization issues investigated by previous work, as it can be formulated as a nonmonotone and non-submodular optimization problem. To address our problem, we propose a novel "surrogate optimization" approach as well as two randomized algorithms which can find approximation solutions with constant performance ratios with high probability. We evaluate the performance of our approach using real social networks. The extensive experimental results demonstrate that our proposed approach significantly outperforms previous work both on the revenue and on the running time.

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