Matrix Factorization-based Recommender System

A Matrix Factorization-based Recommender System is a recommender system that is based on a matrix factorization system and can solve a matrix factorization-based item recommendation task.

• Example(s):
  • one based on Spark.ALS.
  • …
• Counter-Example(s):
  • User-based Collaborative Filtering Recommender System.
  • Item-based Collaborative Filtering Recommender System.
• See: Item Recommendation Task, Baseline Recommender System.

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References

2012

• http://slideshare.net/studentalei/matrix-factorization-techniques-for-recommender-systems

2010

• (Jamali & Ester, 2010) ⇒ Mohsen Jamali, and Martin Ester. (2010). “A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks.” In: Proceedings of the fourth ACM conference on Recommender systems. ISBN:978-1-60558-906-0 doi:10.1145/1864708.1864736
  • QUOTE: … As one of their major benefits, social network based approaches have been shown to reduce the problems with cold start users. In this paper, we explore a model-based approach for recommendation in social networks, employing matrix factorization techniques. Advancing previous work, we incorporate the mechanism of trust propagation into the model. ...

2010

• http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/
  • QUOTE: … Recommendations can be generated by a wide range of algorithms. While user-based or item-based collaborative filtering methods are simple and intuitive, matrix factorization techniques are usually more effective because they allow us to discover the latent features underlying the interactions between users and items. ...

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