36,073 research outputs found
Algorithms and Architecture for Real-time Recommendations at News UK
Recommendation systems are recognised as being hugely important in industry,
and the area is now well understood. At News UK, there is a requirement to be
able to quickly generate recommendations for users on news items as they are
published. However, little has been published about systems that can generate
recommendations in response to changes in recommendable items and user
behaviour in a very short space of time. In this paper we describe a new
algorithm for updating collaborative filtering models incrementally, and
demonstrate its effectiveness on clickstream data from The Times. We also
describe the architecture that allows recommendations to be generated on the
fly, and how we have made each component scalable. The system is currently
being used in production at News UK.Comment: Accepted for presentation at AI-2017 Thirty-seventh SGAI
International Conference on Artificial Intelligence. Cambridge, England 12-14
December 201
Efficient Multicore Collaborative Filtering
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 size.To 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.Comment: In ACM KDD CUP Workshop 201
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