Recsys 2016 Tutorial On Group

Media Summary: Ido Guy, Luiz Pizzato People recommenders have become a rich research area within ... Xavier Amatriain, Deepak Agarwal In 2006, Netflix announced a \$1M prize competition ... Bartłomiej Twardowski Preparing recommendations for unknown users or such that ...

Recsys 2016 Tutorial On Group - Detailed Analysis & Overview

Ido Guy, Luiz Pizzato People recommenders have become a rich research area within ... Xavier Amatriain, Deepak Agarwal In 2006, Netflix announced a \$1M prize competition ... Bartłomiej Twardowski Preparing recommendations for unknown users or such that ... Elisa Quintarelli, Emanuele Rabosio, Letizia Tanca Bart P. Knijnenburg, Saadhika Sivakumar, Daricia Wilkinson Every day, we are ... Marco Rossetti, Fabio Stella, Markus Zanker Most evaluations of novel algorithmic ...

Shameem A. Puthiya Parambath, Nicolas Usunier, Yves Grandvalet We consider the ... Patrick Shafto, Olfa Nasraoui We bring to the fore of the recommender system research ... Sujoy Roy, Sharath Chandra Guntuku Recommending items that have rarely/never ...

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RecSys 2016: Tutorial on Group Recommender Systems

RecSys 2016: Tutorial on People Recommendation

RecSys 2016: Tutorial on Lessons Learned from Building Real-life Recommender Systems

RecSys 2016: Paper Session 9 - Modelling Contextual Information in Session-Aware Recommender Systems

RecSys 2016: Tutorial on Matrix and Tensor Decomposition

RecSys 2016: Paper Session 10 - Recommending New Items to Ephemeral Groups

Tutorial 3C Offline Evaluation for Group Recommender Systems

RecSys 2016: Paper Session1 - Recommender Systems Self Actualization

RecSys 2016: Paper Session 1 - Contrasting Offline and Online Results when Evaluating Recommendation

RecSys 2016: Paper Session 1 - A Coverage-Based Approach to Recommendation

RecSys 2016 Opening Remarks

RecSys 2016: Paper Session 4 - Human-Recommender Systems: From Benchmark Data to Benchmark

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RecSys 2016: Tutorial on Group Recommender Systems

RecSys 2016: Tutorial on Group Recommender Systems

Ludovico Boratto https://doi.org/10.1145/2959100.2959197

RecSys 2016: Tutorial on People Recommendation

RecSys 2016: Tutorial on People Recommendation

Ido Guy, Luiz Pizzato https://doi.org/10.1145/2959100.2959196 People recommenders have become a rich research area within ...

RecSys 2016: Tutorial on Lessons Learned from Building Real-life Recommender Systems

RecSys 2016: Tutorial on Lessons Learned from Building Real-life Recommender Systems

Xavier Amatriain, Deepak Agarwal https://doi.org/10.1145/2959100.2959194 In 2006, Netflix announced a \$1M prize competition ...

RecSys 2016: Paper Session 9 - Modelling Contextual Information in Session-Aware Recommender Systems

RecSys 2016: Paper Session 9 - Modelling Contextual Information in Session-Aware Recommender Systems

Bartłomiej Twardowski https://doi.org/10.1145/2959100.2959162 Preparing recommendations for unknown users or such that ...

RecSys 2016: Tutorial on Matrix and Tensor Decomposition

RecSys 2016: Tutorial on Matrix and Tensor Decomposition

Panagiotis Symeonidis https://doi.org/10.1145/2959100.2959195 This

RecSys 2016: Paper Session 10 - Recommending New Items to Ephemeral Groups

RecSys 2016: Paper Session 10 - Recommending New Items to Ephemeral Groups

Elisa Quintarelli, Emanuele Rabosio, Letizia Tanca https://doi.org/10.1145/2959100.2959137

Tutorial 3C Offline Evaluation for Group Recommender Systems

Tutorial 3C Offline Evaluation for Group Recommender Systems

RecSys

RecSys 2016: Paper Session1 - Recommender Systems Self Actualization

RecSys 2016: Paper Session1 - Recommender Systems Self Actualization

Bart P. Knijnenburg, Saadhika Sivakumar, Daricia Wilkinson https://doi.org/10.1145/2959100.2959189 Every day, we are ...

RecSys 2016: Paper Session 1 - Contrasting Offline and Online Results when Evaluating Recommendation

RecSys 2016: Paper Session 1 - Contrasting Offline and Online Results when Evaluating Recommendation

Marco Rossetti, Fabio Stella, Markus Zanker https://doi.org/10.1145/2959100.2959176 Most evaluations of novel algorithmic ...

RecSys 2016: Paper Session 1 - A Coverage-Based Approach to Recommendation

RecSys 2016: Paper Session 1 - A Coverage-Based Approach to Recommendation

Shameem A. Puthiya Parambath, Nicolas Usunier, Yves Grandvalet https://doi.org/10.1145/2959100.2959149 We consider the ...

RecSys 2016 Opening Remarks

RecSys 2016 Opening Remarks

Werner Geyer and Shilad Sen http://dl.acm.org/citation.cfm?id=3057279.

RecSys 2016: Paper Session 4 - Human-Recommender Systems: From Benchmark Data to Benchmark

RecSys 2016: Paper Session 4 - Human-Recommender Systems: From Benchmark Data to Benchmark

Patrick Shafto, Olfa Nasraoui https://doi.org/10.1145/2959100.2959188 We bring to the fore of the recommender system research ...

RecSys 2016: Paper Session 3 - Latent Factor Representations for Cold-Start Video Recommendation

RecSys 2016: Paper Session 3 - Latent Factor Representations for Cold-Start Video Recommendation

Sujoy Roy, Sharath Chandra Guntuku https://doi.org/10.1145/2959100.2959172 Recommending items that have rarely/never ...