ViTOR: Learning to Rank Webpages Based on Visual Features by Bram van den Akker, Ilya Markov, and Maarten de Rijke is available online now at this location. The visual appearance of a webpage carries valuable information about the page’s quality and can be used to improve the performance of learning to rank (LTR). We introduce the Visual…
Author: mdr
ECIR 2019 paper on information-seeking dialogues online
QRFA: A data-driven model of information-seeking dialogues by Svitlana Vakulenko, Kate Revoredo, Claudio Di Ciccio, and Maarten de Rijke is available online now at this location. Understanding the structure of interaction processes helps us to improve information-seeking dialogue systems. Analyzing an interaction process boils down to discovering patterns in sequences of alternating utterances exchanged between a…
ECIR 2019 paper on online learning to rank online
Optimizing ranking models in an online setting by Harrie Oosterhuis and Maarten de Rijke is available online now at this location. Online Learning to Rank (OLTR) methods optimize ranking models by directly interacting with users, which allows them to be very efficient and responsive. All OLTR methods introduced during the past decade have extended on the…
WSDM 2019 paper on off-policy evaluation online
When people change their mind: Off-policy evaluation in non-stationary recommendation environments by Rolf Jagerman, Ilya Markov, and Maarten de Rijke is online now at this location. We consider the novel problem of evaluating a recommendation policy offline in environments where the reward signal is non- stationary. Non-stationarity appears in many Information Retrieval (IR) applications such as…
WSDM 2019 paper on open-domain question answering online
Learning to transform, combine, and reason in open-domain question answering by Mostafa Dehghani, Hosein Azarbonyad, Jaap Kamps, and Maarten de Rijke is online now at this location. Users seek direct answers to complex questions from large open-domain knowledge sources like the Web. Open-domain question answering has become a critical task to be solved for building systems…
AAAI 2019 paper on repeat aware recommendation online
RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation by Pengjie Ren, Zhumin Chen, Jing Li, Zhaochun Ren, Jun Ma, and Maarten de Rijke is online now at this location. Recurrent neural networks for session-based recommendation have attracted a lot of attention recently because of their promising performance. Repeat consumption is a common phenomenon in many recommendation…
AAAI 2019 paper on dialogue generation online
Dialogue generation: From imitation learning to inverse reinforcement learning by Ziming Li, Julia Kiseleva, and Maarten de Rijke is online now at this location. The performance of adversarial dialogue generation models relies on the quality of the reward signal produced by the discriminator. The reward signal from a poor discriminator can be very sparse and unstable,…
Come work with us. We’re hiring again
Come work with us. We’re hiring again: One PhD student in AI and IR, to work on multi-modal search, see this page for details Two PhD students in AI and Operations Research, to work on replenishment, see this page for details Contact me at derijke@uva.nl if you’d like to find out more. I particularly encourage…
Paper on incremental sparse Bayesian ordinal regression published in Neural Networks
“Incremental sparse Bayesian ordinal regression” by Chang Li and Maarten de Rijke has been published in the October 2018 issue of Neural Networks. See the journal’s site. Ordinal Regression (OR) aims to model the ordering information between different data categories, which is a crucial topic in multi-label learning. An important class of approaches to OR…
CIKM 2018 paper on Web-based Startup Success Prediction online
Web-based Startup Success Prediction by Boris Sharchilev, Michael Roizner, Andrey Rumyantsev, Denis Ozornin, Pavel Serdyukov, Maarten de Rijke is online now at this page. In the paper we consider the problem of predicting the success of startup companies at their early development stages. We formulate the task as predicting whether a company that has already…