Local approaches for collaborative filtering.
Degree: PhD, Computer Science, 2015, Georgia Tech
Recommendation systems are emerging as an important business application as the demand for personalized services in E-commerce increases. Collaborative filtering techniques are widely used for predicting a user's preference or generating a list of items to be recommended. In this thesis, we develop several new approaches for collaborative filtering based on model combination and kernel smoothing. Specifically, we start with an experimental study that compares a wide variety of CF methods under different conditions. Based on this study, we formulate a combination model similar to boosting but where the combination coefficients are functions rather than constant. In another contribution we formulate and analyze a local variation of matrix factorization. This formulation constructs multiple local matrix factorization models and then combines them into a global model. This formulation is based on the local low-rank assumption, a slightly different but more plausible assumption about the rating matrix. We apply this assumption to both rating prediction and ranking problems, with both empirical validations and theoretical analysis.
We contribute with this thesis in four aspects. First, the local approaches we present significantly improve the accuracy of recommendations both in rating prediction and ranking problems. Second, with the more realistic local low-rank assumption, we fundamentally change the underlying assumption for matrix factorization-based recommendation systems. Third, we present highly efficient and scalable algorithms which take advantage of parallelism, suited for recent large scale datasets. Lastly, we provide an open source software implementing the local approaches in this thesis as well as many other recent recommendation algorithms, which can be used both in research and production.
Advisors/Committee Members: Chau, Polo (advisor), Lebanon, Guy (committee member), Zha, Hongyuan (committee member), Song, Le (committee member), Singer, Yoram (committee member).
Subjects/Keywords: Recommendation systems; Collaborative filtering; Machine learning; Local low-rank assumption; Matrix factorization; Matrix approximation; Ensemble collaborative ranking
to Zotero / EndNote / Reference
APA (6th Edition):
Lee, J. (2015). Local approaches for collaborative filtering. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53846
Chicago Manual of Style (16th Edition):
Lee, Joonseok. “Local approaches for collaborative filtering.” 2015. Doctoral Dissertation, Georgia Tech. Accessed April 19, 2019.
MLA Handbook (7th Edition):
Lee, Joonseok. “Local approaches for collaborative filtering.” 2015. Web. 19 Apr 2019.
Lee J. Local approaches for collaborative filtering. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Apr 19].
Available from: http://hdl.handle.net/1853/53846.
Council of Science Editors:
Lee J. Local approaches for collaborative filtering. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53846