Recsys 2016 Paper Session 11

Media Summary: Chao-Yuan Wu, Christopher V. Alvino, Alexander J. Smola, Justin Basilico Implicit ... Rose Catherine, William Cohen Improving the performance of recommender systems ... Babak Loni, Roberto Pagano, Martha Larson, Alan Hanjalic Pairwise learning-to-rank ...

Recsys 2016 Paper Session 11 - Detailed Analysis & Overview

Chao-Yuan Wu, Christopher V. Alvino, Alexander J. Smola, Justin Basilico Implicit ... Rose Catherine, William Cohen Improving the performance of recommender systems ... Babak Loni, Roberto Pagano, Martha Larson, Alan Hanjalic Pairwise learning-to-rank ... Ramon Lopes, Renato Assunção, Rodrygo L.T. Santos Short-length random walks on ... Mike Gartrell, Ulrich Paquet, Noam Koenigstein Determinantal point processes (DPPs) ... Bart P. Knijnenburg, Saadhika Sivakumar, Daricia Wilkinson Every day, we are ...

Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, Hwanjo Yu Sparseness of ... Sujoy Roy, Sharath Chandra Guntuku Recommending items that have rarely/never ... Ramesh Baral, Tao Li The evolution of the World Wide Web (WWW) and the ... Xavier Amatriain, Deepak Agarwal In 2006, Netflix announced a \$1M prize competition ... Shuo Chang, F. Maxwell Harper, Loren Gilbert Terveen Explanations are important for ... Ludovico Boratto Group recommender systems provide suggestions in contexts in ...

Shameem A. Puthiya Parambath, Nicolas Usunier, Yves Grandvalet We consider the ...

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RecSys 2016: Paper Session 11 - Using Navigation to Improve Recommendations in Real-Time

RecSys 2016: Paper Session 11 - Personalized Recommendations using Knowledge Graphs

RecSys 2016: Paper Session 11 - Bayesian Personalized Ranking with Multi-Channel User Feedback

RecSys 2016: Paper Session 11 - Efficient Bayesian Methods for Graph-based Recommendation

RecSys 2016: Paper Session 11 - Bayesian Low-Rank Determinantal Point Processes

RecSys 2016: Paper Session1 - Recommender Systems Self Actualization

RecSys 2016: Paper Session 8 - Convolutional Matrix Factorization for Context-Aware Recommendation

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

RecSys 2016: Paper Session 9 - MAPS: A Multi Aspect Personalized POI Recommender System

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

RecSys 2016: Paper Session 5 - Crowd-Based Personalized Natural Language Explanations

RecSys 2016: Tutorial on Group Recommender Systems

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RecSys 2016: Paper Session 11 - Using Navigation to Improve Recommendations in Real-Time

RecSys 2016: Paper Session 11 - Using Navigation to Improve Recommendations in Real-Time

Chao-Yuan Wu, Christopher V. Alvino, Alexander J. Smola, Justin Basilico https://doi.org/10.1145/2959100.2959174 Implicit ...

RecSys 2016: Paper Session 11 - Personalized Recommendations using Knowledge Graphs

RecSys 2016: Paper Session 11 - Personalized Recommendations using Knowledge Graphs

Rose Catherine, William Cohen https://doi.org/10.1145/2959100.2959131 Improving the performance of recommender systems ...

RecSys 2016: Paper Session 11 - Bayesian Personalized Ranking with Multi-Channel User Feedback

RecSys 2016: Paper Session 11 - Bayesian Personalized Ranking with Multi-Channel User Feedback

Babak Loni, Roberto Pagano, Martha Larson, Alan Hanjalic https://doi.org/10.1145/2959100.2959163 Pairwise learning-to-rank ...

RecSys 2016: Paper Session 11 - Efficient Bayesian Methods for Graph-based Recommendation

RecSys 2016: Paper Session 11 - Efficient Bayesian Methods for Graph-based Recommendation

Ramon Lopes, Renato Assunção, Rodrygo L.T. Santos https://doi.org/10.1145/2959100.2959132 Short-length random walks on ...

RecSys 2016: Paper Session 11 - Bayesian Low-Rank Determinantal Point Processes

RecSys 2016: Paper Session 11 - Bayesian Low-Rank Determinantal Point Processes

Mike Gartrell, Ulrich Paquet, Noam Koenigstein https://doi.org/10.1145/2959100.2959178 Determinantal point processes (DPPs) ...

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 8 - Convolutional Matrix Factorization for Context-Aware Recommendation

RecSys 2016: Paper Session 8 - Convolutional Matrix Factorization for Context-Aware Recommendation

Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, Hwanjo Yu https://doi.org/10.1145/2959100.2959165 Sparseness of ...

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 9 - MAPS: A Multi Aspect Personalized POI Recommender System

RecSys 2016: Paper Session 9 - MAPS: A Multi Aspect Personalized POI Recommender System

Ramesh Baral, Tao Li https://doi.org/10.1145/2959100.2959187 The evolution of the World Wide Web (WWW) and the ...

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 5 - Crowd-Based Personalized Natural Language Explanations

RecSys 2016: Paper Session 5 - Crowd-Based Personalized Natural Language Explanations

Shuo Chang, F. Maxwell Harper, Loren Gilbert Terveen https://doi.org/10.1145/2959100.2959153 Explanations are important for ...

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 ...