HONG KONG BAPTIST UNIVERSITY
FACULTY OF SCIENCE
Department of Computer Science Colloquium
From Influence to Revenue: Incentivized Social Advertising
Prof. Laks V.S. Lakshmanan
Department of Computer Science
University of British Columbia
Date: August 16, 2017 (Wednesday)
Time: 11:00 am - 12:00 pm
Venue: SCT909, Cha Chi Ming Science Tower, Ho Sin Hang Campus
Incentivized social advertising is a marketing model that provides monetization opportunities both to the owner of a social networking platform and to the influential users selected in a campaign. Consider a social network owner that sells ad-engagements to advertisers by inserting their ads, in the form of promoted posts, into the feeds of carefully selected “initial endorsers” or seed users: these users receive monetary incentives in exchange for their endorsements. The endorsements help propagate the ads to the feeds of their followers. Whenever any user of the platform engages with an ad, the host is paid some fixed amount by the advertiser, and the ad further propagates to the feed of her followers, potentially recursively. In this context, a key problem for the host is to allocate ads to influential users. Ads propagate virtally, and more so with more influential seed users. On the other hand, such users may cost more as their payment is commensurate with their influence potential. The host must find an allocation that balances between these and maximizes its own revenue, while staying within the budget of each of the advertisers.
This is a challenging problem. I will show that this problem corresponds to the problem of monotone submodular function maximization, subject to a partition matroid constraint on the ads-to- seeds allocation, and to submodular knapsack constraints on the advertisers’ budgets. This problem is NP-hard. I will then discuss two greedy algorithms with provable approximation guarantees, one of which pays close attention to seed user incentive costs. I will discuss results of an extensive set of experiments and conclude the talk with open problems.
Laks V.S. Lakshmanan is a Professor of Computer Science at the University of British Columbia, Vancouver, BC, Canada. His research covers a wide spectrum of topics including data management and mining, advanced data models for novel applications, OLAP and data warehousing, data integration, data cleaning, semi-structured data and XML, information and social networks and social media, recommender systems, and personalization. He is an Associated Editor of the VLDB Journal (VLDBJ) and the Information Systems Journal. He was named as ACM Distinguished Scientist in 2016.
********* ALL INTERESTED ARE WELCOME ***********
(For enquiry, please contact Computer Science Department at 3411 2385)
Department of Computer Science, Hong Kong Baptist University