My personal highlights among the oral paper presentations:
- Chia-Jung Lee et al. – An Optimization Framework for Merging Multiple Result Lists. The authors present a neural network-based approach to learning optimal result list fusion parameters for federated search.
- David Maxwell et al. – Searching and Stopping: An Analysis of Stopping Rules and Strategies. The authors investigate different models of search session termination, aiming to determine the point at which the user stops scanning the result list. To this end, they rely on behavioral theories of frustration and disgust.
- Alessandro Sordoni et al. – A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion. This paper describes the use of hierarchical de-/encoders for query suggestion. Generating a word at a time, the method aims at suggesting contextualised query candidates while ensuring robustness to candidate frequency, making it an interesting option for tail information needs.
- Tom Kenter et al. – Ad Hoc Monitoring of Vocabulary Shifts over Time. The authors describe a distributional semantics approach to characterizing transient word meanings over time. Relying on semantics-preserving word embeddings, they are able to track changing term interpretations as well as changing terminology for the same concept as language and society evolve.
- Daan Odijk et al. – Struggling and Success in Web Search. (Best Student Paper) The paper describes a large-scale empirical study of search success as well as struggles in finding the desired content. The experiment leads to the development of a number of practical techniques for forecasting future user actions, ultimately allowing to support those users with systematic search strategy deficiencies.