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.
A number of recent systems explore the use of crowds for making and executing plans @cite_0 , @cite_16 , @cite_12 , and @cite_7 . Prior work on community-sourcing has shown that community-sourcing can successfully elicit high-quality expert work from specific communities. @cite_5 show that students can grade exams with 2 Another thread of research explored automatically generating an optimal schedule. In the context of conference scheduling, @cite_14 introduced formulations for maximizing the number of talks of interest attendees can attend. However, @cite_2 identified that while automated scheduling is appropriate when the parameters and constraints of the optimization problem are well-specified, interviews with past CHI organizers show that they attempt to tackle soft constraints and other tacit considerations. We discuss how attendee-sourcing data could be useful for automatically generating a draft schedule, which the organizers can refine with their domain expertise and subjective considerations.
@cite_14 @cite_7 @cite_0 @cite_2 @cite_5 @cite_16 @cite_12
2132962756 2108376501 2115312539 2034464625
Online labor markets, such as Amazon's Mechanical Turk, have been used to crowdsource simple, short tasks like image labeling and transcription. However, expert knowledge is often lacking in such markets, making it impossible to complete certain classes of tasks. In this work we introduce an alternative mechanism for crowdsourcing tasks that require specialized knowledge or skill: communitysourcing --- the use of physical kiosks to elicit work from specific populations. We investigate the potential of communitysourcing by designing, implementing and evaluating Umati: the communitysourcing vending machine. Umati allows users to earn credits by performing tasks using a touchscreen attached to the machine. Physical rewards (in this case, snacks) are dispensed through traditional vending mechanics. We evaluated whether communitysourcing can accomplish expert work by using Umati to grade Computer Science exams. We placed Umati in a university Computer Science building, targeting students with grading tasks for snacks. Over one week, 328 unique users (302 of whom were students) completed 7771 tasks (7240 by students). 80 of users had never participated in a crowdsourcing market before. We found that Umati was able to grade exams with 2 higher accuracy (at the same price) or at 33 lower cost (at equivalent accuracy) than traditional single-expert grading. Mechanical Turk workers had no success grading the same exams. These results indicate that communitysourcing can successfully elicit high-quality expert work from specific communities. Effectively planning a large multi-track conference requires an understanding of the preferences and constraints of organizers, authors, and attendees. Traditionally, the onus of scheduling the program falls on a few dedicated organizers. Resolving conflicts becomes difficult due to the size and complexity of the schedule and the lack of insight into community members' needs and desires. Cobi presents an alternative approach to conference scheduling that engages the entire community in the planning process. Cobi comprises (a) communitysourcing applications that collect preferences, constraints, and affinity data from community members, and (b) a visual scheduling interface that combines communitysourced data and constraint-solving to enable organizers to make informed improvements to the schedule. This paper describes Cobi's scheduling tool and reports on a live deployment for planning CHI 2013, where organizers considered input from 645 authors and resolved 168 scheduling conflicts. Results show the value of integrating community input with an intelligent user interface to solve complex planning tasks. Creating a good schedule for a large conference such as CHI requires taking into account the preferences and constraints of organizers, authors, and attendees. Traditionally, the onus of planning is placed entirely on the organizers and involves only a few individuals. Cobi presents an alternative approach to conference scheduling that engages the entire community to take active roles in the planning process. The Cobi system 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 toward improving the schedule based on collected information. We are currently piloting Cobi as part of the CHI 2013 planning process. Automatic review assignment can significantly improve the productivity of many people such as conference organizers, journal editors and grant administrators. Most previous works have set the problem up as using a paper as a query to independently "retrieve" a set of reviewers that should review the paper. A more appropriate formulation of the problem would be to simultaneously optimize the assignments of all the papers to an entire committee of reviewers under constraints such as the review quota. In this paper, we solve the problem of committee review assignment with multi-aspect expertise matching by casting it as an integer linear programming problem. The proposed algorithm can naturally accommodate any probabilistic or deterministic method for modeling multiple aspects to automate committee review assignments. Evaluation using an existing data set shows that the proposed algorithm is effective for committee review assignments based on multi-aspect expertise matching.