Collaborative filtering using rHadoop

Speaker: Chen BinBin


About the Speaker: Binbin Chen obtained his Ph.D degree in Statistics from the Nanyang Technological University i 2013. His research focuses is on high dimensional data problems, includes high dimensional covariance estimation, Bi-dimensional covariance estimation, empirical likelihood, portfolio construction, etc. Now Binbin works as a data scientist in Revolution Analytics. His main role is to mine the value from the data for organization by building statistical models and machine learning algorithms. Binbin has experience in using probabilistic models and machine learning algorithms for texting mining and analyzing time series data, using both parametric and non-parametric time series method for forecasting. 

Synopsis: As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make personalized recommendations or predictions of the unknown preferences for other users. The "rHadoop" project, started by Revolution Analytics, brings the power Hadoop to data scientist, statisticians, data analysts who lack java skills for mapreduce. With this powerful tool, data scientists can handle 100G, TB, PB data. In this talk, I'm going to show how to use implement the item-based CF with rmr2.

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  • Kumar H.

    @Binbin Chen and @Julian. Unable to make to this talk. Appreciate if you share the presentation. TIA.

    June 18

  • Joe

    What is TBC?

    1 · June 9

    • A former member
      A former member

      To be confirmed

      June 9

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