Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 30, Issue 5, Pages 908-921Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2017.2779494
Keywords
Social learning; game theory; network externality; social media
Categories
Funding
- Ministry of Science and Technology [MOST 103-2218-E-001-002-MY2, MOST 105-2221-E-001-003-MY3]
- Academia Sinica under Thematic Research Grant
- 111 Project [B17008]
- National Natural Science Foundation of China [61672137, 61602090]
- Thousand Youth Talents Program of China
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Deal selection on Groupon is a typical social learning and decision making process, where the quality of a deal is usually unknown to the customers. The customers must acquire this knowledge through social learning from other social medias such as reviews on Yelp. Additionally, the quality of a deal depends on both the state of the vendor and decisions of other customers on Groupon. How social learning and network externality affect the decisions of customers in deal selection on Groupon is our main interest. We develop a data-driven game-theoretic framework to understand the rational deal selection behaviors cross social medias. The sufficient condition of the Nash equilibrium is identified. A value-iteration algorithm is proposed to find the optimal deal selection strategy. We conduct a year-long experiment to trace the competitions among deals on Groupon and the corresponding Yelp ratings. We utilize the dataset to analyze the deal selection game with realistic settings. Finally, the performance of the proposed social learning framework is evaluated with real data. The results suggest that customers do make decisions in a rational way instead of following naive strategies, and there is still room to improve their decisions with assistance from the proposed framework.
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