My personal highlights among the oral paper presentations:
- Bhaskar Mitra – Exploring Session Context using Distributed Representations of Queries and Reformulations. The authors rely on convolutional neural networks in order to learn semantically similar query reformulation patterns. Each observed reformulation from the log is mapped into the vector space in order to group and forecast reformulations and, subsequently, improve query auto completion accuracy.
- Christina Lioma, Jakob Grue Simonsen, Birger Larsen and Niels Dalum Hansen – Non-Compositional Term Dependence for Information Retrieval. The authors tackle the challenge of estimating term dependencies by means of Markov random fields based on the notion of term compositionality, following the intuition that non-compositional terms show maximal dependence. In this way, they present an alternative to the popular co-occurrence based dependency estimation schemes.
- Diane Kelly and Leif Azzopardi – How many Results per Page? A Study of SERP Size, Search Behavior and User Experience. This paper studies the relationships among the number of results shown on a SERP, search behavior and user experience. The authors instrument the SERP, showing three, six or the standard ten organic links per page, investigating user experience as well as cognitive and physical workload.
- Artem Grotov, Shimon Whiteson and Maarten de Rijke – Bayesian Ranker Comparison based on Historical User Interactions. Instead of relying live comparison of production and candidate rankers, e.g., in an interleaving fashion, the authors propose a Bayesian scheme for estimating performance metrics and confidence levels on the basis of historic interactions. In this way, risky in vivo experiments can be avoided.