طراحی مکانیزم تشویقی پاداش داده برای استفاده کاربران از محتوای تبلیغاتی با در نظر گرفتن اثرات شبکه

نوع مقاله : مقاله برق

نویسندگان

1 دانشجوی کارشناسی ارشد، دانشکده مهندسی برق و کامپیوتر، دانشکدگان فنی، دانشگاه تهران

2 دانشجوی دکتری، دانشکده مهندسی برق و کامپیوتر، دانشکدگان فنی، دانشگاه تهران

3 دانشیار، دانشکده مهندسی برق و کامپیوتر، دانشکدگان فنی، دانشگاه تهران

چکیده

پاداش داده یک مدل کسب و کار جدید است که منجر به یک روند اقتصادی در شبکه‌های تلفن‌همراه می‌شود. در این طرح، تبلیغ‌کننده به کاربران تلفن‌همراه انگیزه می‌دهد تا تبلیغات را تماشا و در عوض، پاداشی به صورت داده تلفن‌همراه دریافت کنند. در این کار تعامل بین تبلیغ‌کننده‌‌ای که از کاربران اطلاعات نامتقارن دارد، و کاربرانی که تحت یک شبکه زیربنایی با یکدیگر در ارتباط هستند را با استفاده از رویکرد نظریه قرارداد مدل‌سازی می‌کنیم. سپس، شرایط لازم و کافی برای یک قرارداد بهینه و عملی را بدست می‌آوریم، به طوریکه هم باعث ایجاد انگیزه در کاربران برای شرکت در طرح پاداش داده شود و هم آن‌ها را ترغیب به اعلام صادقانه اطلاعاتشان کند. فرمول‌بندی این قرارداد، به صورت یک مسئله بهینه‌سازی مقیدِ غیرمحدب می‌باشد. با استفاده از لم‌ها و گزاره‌هایی، مسئله بهینه‌سازی اولیه که حل آن چالش برانگیز است را به صورت یک مسئله بهینه‌سازی با قیود محدب فرمول‌بندی کرده و اثبات می‌کنیم که این دو مسئله دقیقا معادل یکدیگر هستند. در نهایت، با ارزیابی‌های عددی گسترده‌، کارایی عملکرد طرح پاداش داده را در مقایسه با طرح‌های معیار، نشان می‌دهیم.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Designing an Incentive Mechanism to Reward Users for Advertising Content Usage Considering Network Effects

نویسندگان [English]

  • Hamed Kebriaei 1
  • Alireza Baneshi 2
  • Mina Montazeri 3
1 MSc Student, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
2 PhD Student, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
3 Associate Professor, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
چکیده [English]

Data rewarding is a novel business model that leads to an economic trend in mobile networks. In this scheme, the advertiser incentivizes users to watch ads and, in return, receive a reward in the form of mobile data. In this work, we model the interaction between an advertiser who has asymmetric information about users and users who are connected to each other under an underlying network, using the contract theory approach. Then, we obtain the necessary and sufficient conditions for an optimal and practical contract to motivate users to participate in the reward scheme and encourage them to declare their information truthfully. The formulation of this contract is a non-convex-constrained optimization problem. Using lemmas and propositions, we formulate the initial optimization problem that is challenging to solve as an optimization problem with convex constraints and prove that these two problems are equivalent to each other. Finally, with extensive numerical evaluations, we demonstrate the performance efficiency of the data rewarding scheme compared to benchmark schemes.

کلیدواژه‌ها [English]

  • Asymmetric information
  • Contract theory
  • Data rewarding
  • Incentive compatibility
  • Network effects

مراجع

[1] Analytics, Strategy. Worldwide cellular user forecasts 2018-2023. Tech. Rep, 2018.
[2] T. Dimitriou, and Antonis Michalas. "Incentivizing participation in crowd-sensing applications through fair and private bitcoin rewards." IEEE Access 10 (2022): 129004-129018.
[3] Q. Cheng, H. Shan, W. Zhuang, T.Q. Quek, Z. Zhang, and F. Hou. "When virtual network operator meets e-commerce platform: Advertising via data reward." IEEE Transactions on Mobile Computing 22, no. 12 (2022): 7370 - 7386.
[4] C. He, T.H. Luan, N. Cheng, G. Wei, Z. Su, and Y. Liu. "Federated Learning based Vehicular Threat Sharing: A Multi-Dimensional Contract Incentive Approach." In 2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall), pp. 1-5. IEEE, 2023.
[5] Z. Xiong, J. Kang, D. Niyato, P. Wang, H. Vincent Poor, and S. Xie. "A multi-dimensional contract approach for data rewarding in mobile networks." IEEE Transactions on Wireless Communications 19, no. 9 (2020): 5779-5793.
[6] J. Nie, J. Luo, Z. Xiong, D. Niyato, P. Wang, and M. Guizani. "An incentive mechanism design for socially aware crowdsensing services with incomplete information." IEEE Communications Magazine 57, no. 4 (2019): 74-80.
[7] P. Bolton, and M. Dewatripont. Contract theory. MIT press, 2004.
[8] T. Roughgarden. "Algorithmic game theory." Communications of the ACM 53, no. 7 (2010): 78-86.
[9] Y. Zhang, M. Pan, L. Song, Z. Dawy, and Z. Han. "A survey of contract theory-based incentive mechanism design in wireless networks." IEEE Wireless Communications 24, no. 3 (2017): 80-85.
[10] Y. Liu, M. Tian, Y. Chen, Z. Xiong, C. Leung, and C. Miao. "A contract theory based incentive mechanism for federated learning." In Federated and Transfer Learning, pp. 117-137. Cham: Springer International Publishing, 2022.
[11] M. Diamanti, P. Charatsaris, E.E. Tsiropoulou, and S. Papavassiliou. "Incentive mechanism and resource allocation for edge-fog networks driven by multi-dimensional contract and game theories." IEEE Open Journal of the Communications Society 3 (2022): 435-452.
[12] M. Wu, D. Ye, J. Ding, Y. Guo, R. Yu, and M. Pan. "Incentivizing differentially private federated learning: A multidimensional contract approach." IEEE Internet of Things Journal 8, no. 13 (2021): 10639-10651.
[13] H. Yu, M.H. Cheung, L. Gao, and J. Huang. "Public Wi-Fi monetization via advertising." IEEE/ACM Transactions on Networking 25, no. 4 (2017): 2110-2121.
[14] H. Guo, X. Zhao, L. Hao, and D. Liu. "Economic analysis of reward advertising." Production and Operations Management 28, no. 10 (2019): 2413-2430.
[15] P. Bangera, S. Hasan, and S. Gorinsky. "An advertising revenue model for access ISPs." In 2017 IEEE Symposium on Computers and Communications (ISCC), pp. 582-589. IEEE, 2017.
[16] S. Sen, G. Burtch, A. Gupta, and R. Rill. "Incentive design for ad-sponsored content: Results from a randomized trial." In 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 826-831. IEEE, 2017.
[17] H. Yu, E. Wei, and R.A. Berry. "Monetizing mobile data via data rewards." IEEE Journal on Selected Areas in Communications 38, no. 4 (2020): 782-792.
[18] J. Kang, Z. Xiong, D. Niyato, S. Xie, and J. Zhang. "Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory." IEEE Internet of Things Journal 6, no. 6 (2019): 10700-10714.
[19] H. Yu, E. Wei, and R.A. Berry. "A business model analysis of mobile data rewards." In IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pp. 2098-2106. IEEE, 2019.
[20] J. Nie, J. Luo, Z. Xiong, D. Niyato, and P. Wang. "A stackelberg game approach toward socially-aware incentive mechanisms for mobile crowdsensing." IEEE Transactions on Wireless Communications 18, no. 1 (2018): 724-738.
[21] D. Easley, and J. Kleinberg. Networks, crowds, and markets: Reasoning about a highly connected world. Vol. 1. Cambridge: Cambridge university press, 2010.
[22] A. Jadbabaie, and A. Kakhbod. "Optimal contracting in networks." Journal of Economic Theory 183 (2019): 1094-1153.
[23] Y. Zhang, L. Song, W. Saad, Z. Dawy, and Z. Han. "Contract-based incentive mechanisms for device-to-device communications in cellular networks." IEEE Journal on Selected Areas in Communications 33, no. 10 (2015): 2144-2155.
[24] F. Bloch, and N. Quérou. "Pricing in social networks." Games and Economic Behavior 80 (2013): 243-261.
[25] O. Candogan, K. Bimpikis, and A. Ozdaglar. "Optimal pricing in networks with externalities." Operations Research 60, no. 4 (2012): 883-905.
[26] M. Montazeri, H. Kebriaei, B.N. Araabi, and D. Niyato. "Optimal mechanism design in the sponsored content service market." IEEE Communications Letters 25, no. 9 (2021): 3051-3054.
[27] A. Majeed, and I. Rauf. "Graph theory: A comprehensive survey about graph theory applications in computer science and social networks." Inventions 5, no. 1 (2020): 10.
 
مدل سازی در مهندسی
دوره 22، شماره 78
آبان 1403
صفحه 259-273

فایل ها

سابقه مقاله

  • تاریخ دریافت: 03 فروردین 1402
  • تاریخ بازنگری: 19 اسفند 1402
  • تاریخ پذیرش: 20 اسفند 1402

هم رسانی

ارجاع به این مقاله

آمار