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1 - 6 of 6
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 More reputable recommenders give more accurate recommendations?
Yuan W., Guan D., Han Y., Lee S., Lee Y. ICUIMC 2013 (Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication, Kota Kinabalu, Malaysia, Jan 17-19, 2013) 1-8, 2013. Type: Proceedings
Predictive analytics has hit the mainstream, thanks to the emergence of many day-to-day consumer applications such as Pandora, Netflix, Yelp, and Epinions, all of which contain some variation of a recommendation engine. These engines (or systems) ...
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May 6 2013 |
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Near-optimal scheduling mechanisms for deadline-sensitive jobs in large computing clusters
Jain N., Menache I., Naor J., Yaniv J. SPAA 2012 (Proceedings of the 24th ACM Symposium on Parallelism in Algorithms and Architectures, Pittsburgh, PA, Jun 25-27, 2012) 255-266, 2012. Type: Proceedings
The nearly simultaneous emergence of cloud computing and big data analytics has brought on new sets of challenges. Organizations have started to replace their own infrastructure with large computing clusters hosted by cloud providers, such as...
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Aug 17 2012 |
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A note on disjoint cycles
Kotrb ík M. Information Processing Letters 112(4): 135-137, 2012. Type: Article
Certain measures of graphs, such as the edge coloring number, are hard to compute precisely. Some others, such as the maximum number of vertex-disjoint cycles, are even hard to approximate. It is known, for example, that computing...
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May 17 2012 |
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Distributed tuning of machine learning algorithms using MapReduce clusters
Ganjisaffar Y., Debeauvais T., Javanmardi S., Caruana R., Lopes C. LDMTA 2011 (Proceedings of the 3rd Workshop on Large Scale Data Mining: Theory and Applications, San Diego, CA, Aug 21, 2011) 1-8, 2011. Type: Proceedings
While machine learning algorithms have been around for a very long time, they invariably have a human component in the form of tuning--that is, finding the right values for parameters specific to the training set. Sometimes this can take a...
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Mar 30 2012 |
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To fill or not to fill: the gas station problem
Khuller S., Malekian A., Mestre J. ACM Transactions on Algorithms 7(3): 1-16, 2011. Type: Article
The traveling salesperson problem (TSP) is a well-known optimization problem; many variations of it are routinely studied. Although sometimes considered to be a theoretical rather than a practical problem, variants of the TSP appear frequently in ...
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Oct 10 2011 |
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Understanding Bloom filter intersection for lazy address-set disambiguation
Jeffrey M., Steffan J. SPAA 2011 (Proceedings of the 23rd ACM Symposium on Parallelism in Algorithms and Architectures, San Jose, CA, Jun 4-6, 2011) 345-354, 2011. Type: Proceedings
Bloom filters have recently received a significant amount of interest due to their many applications in the large data and analytics spaces. The original use of the Bloom filter (by Bloom and in the years that followed) was in the form of a data...
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Aug 19 2011 |
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