Daan Odijk wins the CIKM’15 Best Student Paper Award

logo-uvaILPS-er Daan Odijk received the CIKM’15 Best Student Paper Award for his paper “Struggling and Success in Web Search”. This full paper is the result of his internship at Microsoft Research in Redmond. In this joint work with Daan Odijk, Ryen White, Ahmed Hassan Awadallah and Susan Dumais, they investigate why some web searchers succeed where others struggle. Continue reading

Invited Talk by Emine Yilmaz

On Friday October 16, 16:00-17:00, Emine Yilmaz (University College London, Microsoft Research Cambridge) will give a talk entitled “Task-based Information Retrieval”. Abstract: The need for search often arises from a person’s need to achieve a goal, or a task such as booking travels, organizing a wedding, buying a house, investing in the stock market, etc. Since current search engines focus on retrieving documents relevant to the query submitted as opposed to Continue reading

Google Faculty Research Award for Evangelos Kanoulas

G-symbolILPS-er Evangelos Kanoulas has been awarded a Google Faculty Research Award for his proposal ‘Session-based Personalization: Analysis and Evaluation’, to conduct research on personalizing search engine results on the basis of user interactions with the search engine on the current session. The Google Research Awards Program received 805 strong proposals and funded 113 of them, with only 3 of them in the fields of Information retrieval, extraction and organization (including semantic graphs). More information

Adith Swaminathan and Tobias Schnabel visit ILPS

cornellAdith Swaminathan and Tobias Schnabel are visiting for one year. They are both PhD students at Cornell University working in the intersection of machine learning and information access. Adith’s main focus is on batch learning from bandit feedback using counterfactual risk estimators, developing the learning algorithms and the underlying theory for principled learning from logged interaction data. Tobias works on principled methods for evaluation, including the evaluation of embedding methods for text and the use of propensity scoring for reducing bias in recommender-system evaluation due to self-selection effects.