Media Summary: Xavier Amatriain, Deepak Agarwal In 2006, Netflix announced a \$1M prize competition ... Ido Guy, Luiz Pizzato People recommenders have become a rich research area within ... Ludovico Boratto Group recommender systems provide suggestions in contexts in ...

Recsys 2016 Tutorial Lessons Learned - Detailed Analysis & Overview

Xavier Amatriain, Deepak Agarwal In 2006, Netflix announced a \$1M prize competition ... Ido Guy, Luiz Pizzato People recommenders have become a rich research area within ... Ludovico Boratto Group recommender systems provide suggestions in contexts in ... Shameem A. Puthiya Parambath, Nicolas Usunier, Yves Grandvalet We consider the ... Bartłomiej Twardowski Preparing recommendations for unknown users or such that ... Haokai Lu, James Caverlee, Wei Niu Discovering what people are known for is ...

Bart P. Knijnenburg, Saadhika Sivakumar, Daricia Wilkinson Every day, we are ... Paul Covington, Jay Adams, Emre Sargin YouTube represents one of the largest scale ... Jie Yang, Zhu Sun, Alessandro Bozzon, Jie Zhang Existing feature-based ... Sujoy Roy, Sharath Chandra Guntuku Recommending items that have rarely/never ... Evangelia Christakopoulou, George Karypis Item-based approaches based on SLIM ... Feature Engineering for Recommender Systems by Benedikt Schifferer (Nvidia), Chris Deotte (Nvidia) and Even Oldridge (Nvidia) ...

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RecSys 2016: Tutorial on Lessons Learned from Building Real-life Recommender Systems
RecSys 2016: Tutorial on  Matrix and Tensor Decomposition
RecSys 2016: Tutorial on People Recommendation
RecSys 2016: Tutorial on Group Recommender Systems
RecSys 2016: Paper Session 1 - A Coverage-Based Approach to Recommendation
RecSys 2016: Paper Session 9 - Modelling Contextual Information in Session-Aware Recommender Systems
RecSys 2016: Paper Session 9 - Discovering What You're Known For
RecSys 2016: Paper Session1 - Recommender Systems Self Actualization
RecSys 2016: Paper Session 6 - Deep Neural Networks for YouTube Recommendations
RecSys 2016: Paper Session 2 - Learning Hierarchical Feature Influence for Recommendation
RecSys 2016: Paper Session 3 - Latent Factor Representations for Cold-Start Video Recommendation
RecSys 2016: Paper Session 2 -  Local Item Item Models For Top-N Recommendation
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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: 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: 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 Group Recommender Systems

RecSys 2016: Tutorial on Group Recommender Systems

Ludovico Boratto https://doi.org/10.1145/2959100.2959197 Group recommender systems provide suggestions in contexts in ...

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: 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: Paper Session 9 - Discovering What You're Known For

RecSys 2016: Paper Session 9 - Discovering What You're Known For

Haokai Lu, James Caverlee, Wei Niu https://doi.org/10.1145/2959100.2959146 Discovering what people are known for is ...

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 6 - Deep Neural Networks for YouTube Recommendations

RecSys 2016: Paper Session 6 - Deep Neural Networks for YouTube Recommendations

Paul Covington, Jay Adams, Emre Sargin https://doi.org/10.1145/2959100.2959190 YouTube represents one of the largest scale ...

RecSys 2016: Paper Session 2 - Learning Hierarchical Feature Influence for Recommendation

RecSys 2016: Paper Session 2 - Learning Hierarchical Feature Influence for Recommendation

Jie Yang, Zhu Sun, Alessandro Bozzon, Jie Zhang https://doi.org/10.1145/2959100.2959159 Existing feature-based ...

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 ...

RecSys 2016: Paper Session 2 -  Local Item Item Models For Top-N Recommendation

RecSys 2016: Paper Session 2 - Local Item Item Models For Top-N Recommendation

Evangelia Christakopoulou, George Karypis https://doi.org/10.1145/2959100.2959185 Item-based approaches based on SLIM ...

RecSys 2020 Tutorial: Feature Engineering for Recommender Systems

RecSys 2020 Tutorial: Feature Engineering for Recommender Systems

Feature Engineering for Recommender Systems by Benedikt Schifferer (Nvidia), Chris Deotte (Nvidia) and Even Oldridge (Nvidia) ...