RecSys 2016: Tutorial on Group Recommender Systems

(1:25:25)

RecSys 2016: Tutorial on People Recommendation

(1:38:13)

RecSys 2016: Paper Session 7 - Behaviorism is Not Enough: Better Recommendations

(18:51)

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

(1:41:25)

RecSys 2016: Paper Session 7 - Recommender Systems With Personality

(15:12)

RecSys 2016: Paper Session 1 - A Scalable Approach for Periodical Personalized Recommendations

(9:21)

RecSys 2016 Opening Remarks

(24:13)

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

(9:43)

Actions Speak Louder than Words - Yueming Wang and Jiaqi Zhai | VideoRecSys Workshop | RecSys 2024

(34:24)

Tutorial 3C Offline Evaluation for Group Recommender Systems

(1:59:57)

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

(19:1econd)

RecSys 2020 Tutorial: Introduction to Bandits in Recommender Systems

(1:23:7)

RecSys 2016: Paper Session 7 - Past, Present, \u0026 Future of Recommender Systems: Industry Perspective

(16:11)

Netflix's Recommendation Evolution by Mark Hsiao | VideoRecSys 2023 Workshop | ACM RecSys

(29:49)

User Intents and Journeys in Recommender Systems by Minmin Chen | VideoRecSys Workshop | RecSys 2023

(38:43)

RecSys 2020 Tutorial: Feature Engineering for Recommender Systems

(2:11:)

RecSys 2016: Tutorial on Matrix and Tensor Decomposition

(1:32:47)

RecSys 2020 Tutorial: Counteracting Bias and Increasing Fairness in Search and Recommender Systems

(1:8:1econd)

RecSys 2016: Paper Session 6 - Optimizing Similar Item Recommendations

(10:24)

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

(10:54)

RecSys 2016: Paper Session1 - Recommender Systems Self Actualization

(14:5)

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

(19:53)

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

(19:9)

RecSys 2016: Keynote - Personalization for Google Now

(1:2:26)

RecSys 2016: Paper Session 1 - Adaptive, Personalized Diversity for Visual Discovery

(9:39)

RecSys 2016: Paper Session 3 - Accuracy and Diversity in Cross-domain Recommendations

(11:2)

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

(20:14)

RecSys 2016: Paper Session 9 - TAPER: A Contextual Tensor-Based Approach

(22:6)

RecSys 2016: Paper Session 8 - Parallel Recurrent Neural Network Architectures for Recommendations

(21:4)