Xavier Amatriain – August 2014 – KDD
The Recommender
Problem
Revisited
Xavier Amatriain
Research/Engineering Director @
Netflix
@xamat
Bamshad Mobasher
Professor @
DePaul University
Xavier Amatriain – August 2014 – KDD
Recent/Upcoming Publications
● The Recommender Problem Revisited. KDD and Recsys 2014 Tutorial
● KDD: Big & Personal: data and models behind Netflix recommendations. 2013
● SIGKDD Explorations: Mining large streams of user data for personalized recommendations. 2012
● Recsys: Building industrial-scale real-world recommender systems. 2012
● Recys - Walk the Talk: Analyzing the relation between implicit and explicit feedback for preference elicitation. 2011
● SIGIR – Temporal behavior of CF. 2010
● Web Intelligence – Expert-based CF for music. 2010
● Recsys – Tensor Factorization. 2010
● Mobile HCI – Tourist Recommendation. 2010
● Recsys Handbook (book) – Data mining for recsys. 2010 & Recommender Systems in Industry. 2014
● SIGIR – Wisdom of the Few. 2009
● Recsys – Denoising by re-rating. 2009
● CARS – Implicit context-aware recommendations. 2009
● UMAP – I like it I like it not. 2009
Xavier Amatriain – August 2014 – KDD
Index
1. The Recommender Problem
2. Traditional Recommendation Methods
2.1. Collaborative Filtering
2.2. Content-based Recommendations
2.3. Hybrid Approaches
3. Beyond Traditional Methods
3.1. Learning to Rank
3.2. Similarity
3.3. Deep Learning
3.4. Social Recommendations
3.5. Page Optimization
3.6. Tensor Factorization and Factorization Machines
3.7. MAB Explore/Exploit
4. References
Xavier Amatriain – August 2014 – KDD
1. The Recommender
Problem
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