Abstract
Conferences with contributed talks grouped into multiple concurrent sessions pose an interesting scheduling problem. From an attendee’s perspective, choosing which talks to visit when there are many concurrent sessions is challenging since an individual may be interested in topics that are discussed in different sessions simultaneously. The frequency of topically similar talks in different concurrent sessions is, in fact, a common cause for complaint in post-conference surveys. Here, we introduce a practical solution to the conference scheduling problem by heuristic optimization of an objective function that weighs the occurrence of both topically similar talks in one session and topically different talks in concurrent sessions. Rather than clustering talks based on a limited number of preconceived topics, we employ a topic model to allow the topics to naturally emerge from the corpus of contributed talk titles and abstracts. We then measure the topical distance between all pairs of talks. Heuristic optimization of preliminary schedules seeks to balance the topical similarity of talks within a session and the dissimilarity between concurrent sessions. Using an ecology conference as a test case, we find that stochastic optimization dramatically improves the objective function relative to the schedule manually produced by the program committee. Approximate Integer Linear Programming can be used to provide a partially-optimized starting schedule, but the final value of the discrimination ratio (an objective function used to estimate coherence within a session and disparity between concurrent sessions) is surprisingly insensitive to the starting schedule. Furthermore, we show that, in contrast to the manual process, arbitrary scheduling constraints are straightforward to include. We applied our method to a second biology conference with over 1,000 contributed talks plus scheduling constraints. In a randomized experiment, biologists responded similarly to a machine-optimized schedule and a highly modified schedule produced by domain experts on the conference program committee.
Highlights
Researchers and educators depend upon professional conferences to showcase their work and stay current on the work of their peers
We describe a heuristic solution to the conference scheduling problem that creates optimized conference schedules with multiple concurrent sessions in a fully automated fashion
Stochastic optimization is used to generate schedules according to the discrimination ratio, which simultaneously quantifies within-session coherence and between-session disparity
Summary
Researchers and educators depend upon professional conferences to showcase their work and stay current on the work of their peers Thousands of such conferences are held each year worldwide, and conferences that feature of hundreds of oral presentations are not unusual. Conference scheduling is typically done manually by program organizers who review the large volume of talk submissions, decide which talks are similar to each other, and group similar talks into sessions (Figure 1). They do this based on the information provided by prospective presenters, which invariably includes a title but may include keywords, topic categories and/or an abstract. Since conference attendees typically aim to attend those talks most relevant to their interests, the ideal conference schedule will ensure similarity of topics within a session, and avoid topical conflict among concurrent sessions
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