Travelled to:
1 × Canada
1 × China
1 × France
1 × Germany
1 × Ireland
1 × Portugal
1 × Singapore
1 × Switzerland
1 × United Kingdom
2 × USA
Collaborated with:
G.Dupret M.Lalmas Y.Moshfeghi I.Frommholz K.v.Rijsbergen J.M.Jose H.Zaragoza P.Gallinari D.Agarwal
Talks about:
model (6) retriev (4) user (4) predict (3) inform (3) filter (3) click (3) document (2) collabor (2) quantum (2)
Person: Benjamin Piwowarski
DBLP: Piwowarski:Benjamin
Contributed to:
Wrote 11 papers:
- ECIR-2011-FrommholzPLR #framework #information retrieval #query
- Processing Queries in Session in a Quantum-Inspired IR Framework (IF, BP, ML, KvR), pp. 751–754.
- SIGIR-2011-MoshfeghiPJ #collaboration #semantics #using
- Handling data sparsity in collaborative filtering using emotion and semantic based features (YM, BP, JMJ), pp. 625–634.
- CIKM-2010-PiwowarskiFLR #information retrieval #quantum #what
- What can quantum theory bring to information retrieval (BP, IF, ML, KvR), pp. 59–68.
- ECIR-2010-PiwowarskiFMLR #documentation
- Filtering Documents with Subspaces (BP, IF, YM, ML, KvR), pp. 615–618.
- SIGIR-2010-DupretP #behaviour #precise
- A user behavior model for average precision and its generalization to graded judgments (GD, BP), pp. 531–538.
- ECIR-2009-MoshfeghiAPJ #collaboration #predict #rating #recommendation #semantics
- Movie Recommender: Semantically Enriched Unified Relevance Model for Rating Prediction in Collaborative Filtering (YM, DA, BP, JMJ), pp. 54–65.
- SIGIR-2008-DupretP #predict
- A user browsing model to predict search engine click data from past observations (GD, BP), pp. 331–338.
- CIKM-2007-PiwowarskiZ #modelling #predict
- Predictive user click models based on click-through history (BP, HZ), pp. 175–182.
- SIGIR-2006-PiwowarskiD #evaluation #information retrieval #modelling #xml
- Evaluation in (XML) information retrieval: expected precision-recall with user modelling (EPRUM) (BP, GD), pp. 260–267.
- CIKM-2004-PiwowarskiL #consistency #evaluation #retrieval #xml
- Providing consistent and exhaustive relevance assessments for XML retrieval evaluation (BP, ML), pp. 361–370.
- MLDM-2003-PiwowarskiG #documentation #information retrieval #machine learning
- A Machine Learning Model for Information Retrieval with Structured Documents (BP, PG), pp. 425–438.