TSMO 2018
Workshop on Two-sided Marketplace Optimization: Search, Pricing, Matching & Growth
in conjunction with The 11th ACM International Conference on Web Search and Data Mining (WSDM 2018)
Workshop on Two-sided Marketplace Optimization: Search, Pricing, Matching & Growth
in conjunction with The 11th ACM International Conference on Web Search and Data Mining (WSDM 2018)
In recent years two sided marketplaces have emerged as viable business models in many real world applications. In particular, we have moved from the social network paradigm to a network with two distinct types of participants representing supply and demand of a specific good. Examples industries include but are not limited to accommodation (Airbnb, Booking.com, HomeAway), video content (YouTube, Dailymotion), Ridesharing (Uber, Didi, Lyft), online shops (Etsy, Ebay), music (Soundcloud), app stores (Apple App Store, Google App Store) and job sites (LinkedIn).
The traditional research in most of these industries focused on satisfying the demand. OTAs would sell hotel accommodation, TV networks would broadcast their own content or taxi companies would own their own vehicle fleet. In modern examples like Airbnb, YouTube or Uber the platforms have customers on the supply side as well, and have to optimize their models taking into consideration host preferences, youtube producers and drivers.
The objective of this workshop is to bring practitioners of two-sided marketplaces together and discuss the evolution of ranking, recommendation, matching and growth data mining to account for the dual nature of the problem.
Registration link: http://www.wsdm-conference.org/2018/attending.html
9:00 Introduction and Welcome
9:05 Happy for Two (or Three): Joint Revenue Optimization for 2-Sided Parties for Promoted Listings at Etsy
Liangjie Hong, Head of Data Science Etsy
9:50 Impact of free app promotion on future sales: A case study on Amazon Appstore
Harshal A. Chaudhari and John Byers Boston University
10:10 Shaping the future of two-sided e-health markets: Moving developing from research to deployment to market
Vivian Vimarlund, Nicolas Nikula and Craig Kuziemsky Linköping University & University of Ottawa
10:30 Break
11:00 An Overview of Surge Pricing
Hamid Nazerzadeh, Research Scientist Uber
11:45 Spatio-Temporal Pricing for Ridesharing Platforms
Hongyao Ma, Fei Fang and David C. Parkes, Carnegie Mellon University & Harvard SEAS
12:05 Dynamic Pricing in High-dimensions
Adel Javanmard and Hamid Nazerzadeh, University of Southern California & Uber
12:25 Lunch Break
13:45 Search, Pricing and Marketplace Dynamics at Airbnb
Bar Ifrach, Director of Data Science Homes Airbnb
14:45 Pointwise learning to rank for two-sided market platforms
Toma Gulea, Turo
15:05 Coffee Break
15:20 A Smart Instant Book Filter Model based on Dual-Objective Optimization
Yi Hou and Li Fan, Airbnb
15:40 Learning to Match
Themis Mavridis, Pablo Estevez and Lucas Bernardi Booking.com
16:00 Supporting content decisions with Machine Learning @ Netflix
Kelly Uphoff, Vice President, Content and Marketing Science and Analytics at Netflix
Papers must be submitted in PDF according to the ACM format published in ACM guidelines, selecting the generic "sigconf" sample. Submissions should not exceed 8 pages plus up to one additional page of references.
Dates
Submission - November 28, 2017
Extended Submission - December 6, 2017
Decision - December 16, 2017
Camera-ready - January 15, 2018
Workshop - February 9, 2018
The topics of this workshop include, but not limit to, the following: