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Computer Science > Machine Learning

arXiv:1108.2580 (cs)
[Submitted on 12 Aug 2011 (v1), last revised 17 Aug 2011 (this version, v2)]

Title:Efficient Multicore Collaborative Filtering

Authors:Yao Wu, Qiang Yan, Danny Bickson, Yucheng Low, Qing Yang
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Abstract:This paper describes the solution method taken by LeBuSiShu team for track1 in ACM KDD CUP 2011 contest (resulting in the 5th place). We identified two main challenges: the unique item taxonomy characteristics as well as the large data set this http URL handle the item taxonomy, we present a novel method called Matrix Factorization Item Taxonomy Regularization (MFITR). MFITR obtained the 2nd best prediction result out of more then ten implemented algorithms. For rapidly computing multiple solutions of various algorithms, we have implemented an open source parallel collaborative filtering library on top of the GraphLab machine learning framework. We report some preliminary performance results obtained using the BlackLight supercomputer.
Comments: In ACM KDD CUP Workshop 2011
Subjects: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
Cite as: arXiv:1108.2580 [cs.LG]
  (or arXiv:1108.2580v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.1108.2580

Submission history

From: Danny Bickson [view email]
[v1] Fri, 12 Aug 2011 07:22:08 UTC (34 KB)
[v2] Wed, 17 Aug 2011 11:14:42 UTC (34 KB)
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