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2007 NIPS NeurIPS 2007

COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking

Abstract

In this paper, we consider collaborative filtering as a ranking problem. We present a method which uses Maximum Margin Matrix Factorization and optimizes rank- ing instead of rating. We employ structured output prediction to optimize directly for ranking scores. Experimental results show that our method gives very good ranking scores and scales well on collaborative filtering tasks.

📈 Trend Setter — Recommender Systems
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
🌉 Interdisciplinary Bridge — Data Science & Analytics and Machine Learning
🐣 Hot Topic Early Bird — structured prediction

Authors

Markus Weimer , Alexandros Karatzoglou , Quoc V Le , Quoc V. Le , Alex J. Smola

Topics

Data Science & Analytics > Applications > Recommender Systems Machine Learning > Core Methods > Matrix Factorization

Keywords

structured prediction matrix factorization collaborative filtering learning to rank maximum margin recommender system
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