WSDM paper

Earlier today, Jan Benetka has presented our paper “Anticipating Information Needs Based on Check-in Activity” at the WSDM’17 conference in Cambrigde, UK.

In this work we address the development of a smart personal assistant that is capable of anticipating a user’s information needs based on a novel type of context: the person’s activity inferred from her check-in records on a location-based social network. Our main contribution is a method that translates a check-in activity into an information need, which is in turn addressed with an appropriate information card. This task is challenging because of the large number of possible activities and related information needs, which need to be addressed in a mobile dashboard that is limited in size. Our approach considers each possible activity that might follow after the last (and already finished) activity, and selects the top information cards such that they maximize the likelihood of satisfying the user’s information needs for all possible future scenarios. The proposed models also incorporate knowledge about the temporal dynamics of information needs. Using a combination of historical check-in data and manual assessments collected via crowdsourcing, we show experimentally the effectiveness of our approach.

Presentation slides and resources can be found at

ICTIR 2016 paper online

“Exploiting Entity Linking in Queries for Entity Retrieval,” an upcoming ICTIR 2016 paper by Faegheh Hasibi, Svein Erik Bratsberg, and myself is available online now, along with the source code.

The premise of entity retrieval is to better answer search queries by returning specific entities instead of documents. Many queries mention particular entities; recognizing and linking them to the corresponding entry in a knowledge base is known as the task of entity linking in queries. In this paper we make a first attempt at bringing together these two, i.e., leveraging entity annotations of queries in the entity retrieval model. We introduce a new probabilistic component and show how it can be applied on top of any term-based entity retrieval model that can be emulated in the Markov Random Field framework, including language models, sequential dependence models, as well as their fielded variations. Using a standard entity retrieval test collection, we show that our extension brings consistent improvements over all baseline methods, includ- ing the current state-of-the-art. We further show that our extension is robust against parameter settings.

Update (16/09): Our paper received the Best Paper Honorable Mention Award at the conference. So it is definitely worth checking out ;)


The continuing goal of the Exploiting Semantic Annotations in Information Retrieval (ESAIR) workshop series is to create a forum for researchers interested in the application of semantic annotations for information access tasks. ESAIR’16 sets its focus on personal mobile applications and will be held in conjunction with CIKM’16 at Indianapolis, USA in October.

Important dates:

  • Position paper submission (2+1 pages): Aug 1, 2016
  • Demo submission (4+ pages): Aug 8, 2016
  • Acceptance notification: 22 August, 2016
  • Camera-ready version: 1 September, 2016