TREC

The University of Amsterdam at the TREC 2011 Session Track

We describe the participation of the University of Amsterdam’s ILPS group in the Session track at TREC 2011.

The stream of interactions created by a user engaging with a search system contains a wealth of information. For retrieval purposes, previous interactions can help inform us about a user’s current information need. Building on this intuition, our contribution to this TREC year’s session track focuses on session modeling and learning to rank using session information. In this paper, we present and compare three complementary strategies that we designed for improving retrieval for a current query using previous queries and clicked results: probabilistic session modeling, semantic query modeling, and implicit feedback.

In our experiments we examined three complementary strategies for improving retrieval for a current query. Our first strategy, based on probabilistic session modeling, was the best performing strategy.

Our second strategy, based on semantic query modeling, did less well than we expected, likely due to topic drift from excessively aggressive query expansion. We expect that performance of this strategy would improve by limiting the number of terms and/or improving the probability estimates.

With respect to our third strategy, based on learning from feedback, we found that learning weights for linear weighted combinations of features from an external collection can be beneficial, if characteristics of the collection are similar to the current data. Feedback available in the form of user clicks appeared to be less beneficial. Our run learning from implicit feedback did perform substantially lower than a run where weights were learned from an external collection with explicit feedback using the same learning algorithm and set of features.

  • [PDF] B. Huurnink, R. Berendsen, K. Hofmann, E. Meij, and M. de Rijke, “The University of Amsterdam at the TREC 2011 session track,” in The twentieth text retrieval conference, 2012.
    [Bibtex]
    @inproceedings{TREC:2011:huurnink,
    Author = {Huurnink, Bouke and Berendsen, Richard and Hofmann, Katja and Meij, Edgar and de Rijke, Maarten},
    Booktitle = {The Twentieth Text REtrieval Conference},
    Date-Added = {2011-10-22 12:22:18 +0200},
    Date-Modified = {2013-05-22 11:44:53 +0000},
    Month = {January},
    Series = {TREC 2011},
    Title = {The {University of Amsterdam} at the {TREC} 2011 Session Track},
    Year = {2012}}
TREC

The University of Amsterdam at Trec 2010: Session, Entity, and Relevance Feedback

We describe the participation of the University of Amsterdam’s ILPS group in the session, entity, and relevance feedback track at TREC 2010. In the Session Track we explore the use of blind relevance feedback to bias a follow-up query towards or against the topics covered in documents returned to the user in response to the original query. In the Entity Track REF task we experiment with a window size parameter to limit the amount of context considered by the entity co-occurrence models and explore the use of Freebase for type filtering, entity normalization and homepage finding. In the ELC task we use an approach that uses the number of links shared between candidate and example entities to rank candidates. In the Relevance Feedback Track we experiment with a novel model that uses Wikipedia to expand the query language model.

  • [PDF] M. Bron, J. He, K. Hofmann, E. Meij, M. de Rijke, E. Tsagkias, and W. Weerkamp, “The University of Amsterdam at TREC 2010: session, entity, and relevance feedback,” in The nineteenth text retrieval conference, 2011.
    [Bibtex]
    @inproceedings{TREC:2011:bron,
    Abstract = {We describe the participation of the University of Amsterdam's Intelligent Systems Lab in the web track at TREC 2009. We participated in the adhoc and diversity task. We find that spam is an important issue in the ad hoc task and that Wikipedia-based heuristic optimization approaches help to boost the retrieval performance, which is assumed to potentially reduce spam in the top ranked results. As for the diversity task, we explored different methods. Clustering and a topic model-based approach have a similar performance and both are relatively better than a query log based approach.},
    Author = {M. Bron and He, J. and Hofmann, K. and Meij, E. and de Rijke, M. and Tsagkias, E. and Weerkamp, W.},
    Booktitle = {The Nineteenth Text REtrieval Conference},
    Date-Added = {2011-10-20 11:18:35 +0200},
    Date-Modified = {2012-10-30 09:25:06 +0000},
    Series = {TREC 2010},
    Title = {{The University of Amsterdam at TREC 2010}: Session, Entity, and Relevance Feedback},
    Year = {2011}}