Online bidding and auction are crucial aspects of the online advertising industry. Conventionally, there is only one slot for ad display and most current studies focus on it. Nowadays, multi-slot display advertising is gradually becoming popular where many ads could be displayed in a list and shown as a whole to users. However, multi-slot display advertising leads to different cost-effectiveness. Advertisers have the incentive to adjust bid prices so as to win the most economical ad positions. In this study, we introduce bid shading into multi-slot display advertising for bid price adjustment with a Multi-task End-to-end Bid Shading(MEBS) method. We prove the optimality of our method theoretically and examine its performance experimentally. Through extensive offline and online experiments, we demonstrate the effectiveness and efficiency of our method, and we obtain a 7.01% lift in Gross Merchandise Volume, a 7.42% lift in Return on Investment, and a 3.26% lift in ad buy count.

For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue. Therefore, providing better services for advertisers is essential for the long-term prosperity for e-commerce platforms. To achieve this goal, the ad platform needs to have an in-depth understanding of advertisers in terms of both their marketing intents and satisfaction over the advertising performance, based on which further optimization could be carried out to service the advertisers in the correct direction. In this paper, we propose a novel Deep Satisfaction Prediction Network (DSPN), which models advertiser intent and satisfaction simultaneously. It employs a two-stage network structure where advertiser intent vector and satisfaction are jointly learned by considering the features of advertiser's action information and advertising performance indicators. Experiments on an Alibaba advertisement dataset and online evaluations show that our proposed DSPN outperforms state-of-the-art baselines and has stable performance in terms of AUC in the online environment. Further analyses show that DSPN not only predicts advertisers' satisfaction accurately but also learns an explainable advertiser intent, revealing the opportunities to optimize the advertising performance further.

Constructing a good conference schedule for a large multi-track conference needs to take into account the preferences and constraints of organizers, authors, and attendees. Creating a schedule which has fewer conflicts for authors and attendees, and thematically coherent sessions is a challenging task. Cobi introduced an alternative approach to conference scheduling by engaging the community to play an active role in the planning process. The current Cobi pipeline consists of committee-sourcing and author-sourcing to plan a conference schedule. We further explore the design space of community-sourcing by introducing attendee-sourcing -- a process that collects input from conference attendees and encodes them as preferences and constraints for creating sessions and schedule. For CHI 2014, a large multi-track conference in human-computer interaction with more than 3,000 attendees and 1,000 authors, we collected attendees’ preferences by making available all the accepted papers at the conference on a paper recommendation tool we built called Confer, for a period of 45 days before announcing the conference program (sessions and schedule). We compare the preferences marked on Confer with the preferences collected from Cobi’s author-sourcing approach. We show that attendee-sourcing can provide insights beyond what can be discovered by author-sourcing. For CHI 2014, the results show value in the method and attendees’ participation. It produces data that provides more alternatives in scheduling and complements data collected from other methods for creating coherent sessions and reducing conflicts.

Creating sessions of related papers for a large conference is a complex and time-consuming task. Traditionally, a few conference organizers group papers into sessions manually. Organizers often fail to capture the affinities between papers beyond created sessions, making incoherent sessions difficult to fix and alternative groupings hard to discover. This paper proposes committeesourcing and authorsourcing approaches to session creation (a specific instance of clustering and constraint satisfaction) that tap into the expertise and interest of committee members and authors for identifying paper affinities. During the planning of ACM CHI'13, a large conference on human-computer interaction, we recruited committee members to group papers using two online distributed clustering methods. To refine these paper affinities — and to evaluate the committeesourcing methods against existing manual and automated approaches — we recruited authors to identify papers that fit well in a session with their own. Results show that authors found papers grouped by the distributed clustering methods to be as relevant as, or more relevant than, papers suggested through the existing in-person meeting. Results also demonstrate that communitysourced results capture affinities beyond sessions and provide flexibility during scheduling.

Creating a schedule for a large multi-track conference requires considering the preferences and constraints of organizers, authors, and attendees. Traditionally, a few dedicated organizers manage the size and complexity of the schedule with limited information and coverage. Cobi presents an alternative approach to conference scheduling by engaging the entire community to take active roles in the planning process. It consists of a collection of crowdsourcing applications that elicit preferences and constraints from the community, and software that enable organizers and other community members to take informed actions based on collected information.

With the proliferation of online labor markets and other social computing platforms, online experiments have become a low-cost and scalable way to empirically test hypotheses and mechanisms in both human computation and social science. Yet, despite the potential in designing more powerful and expressive online experiments using multiple subjects, researchers still face many technical and logistical difficulties. We see synchronous and longitudinal experiments involving real-time interaction between participants as a dual-use paradigm for both human computation and social science, and present TurkServer, a platform that facilitates these types of experiments on Amazon Mechanical Turk. Our work has the potential to make more fruitful online experiments accessible to researchers in many different fields.

Behind every search query is a high-level mission that the user wants to accomplish.  While current search engines can often provide relevant information in response to well-specified queries, they place the heavy burden of making a plan for achieving a mission on the user. We take the alternative approach of tackling users' high-level missions directly by introducing a human computation system that generates simple plans, by decomposing a mission into goals and retrieving search results tailored to each goal. Results show that our system is able to provide users with diverse, actionable search results and useful roadmaps for accomplishing their missions.