Recommendations narrow what could become a complex decision to just a few recommendations. Big Data allowed us to do recommendations on a new scale that we did not see before. The most well-known example is how the Google search algorithm trumped Altavista by recommending the best websites to view. Another well-known example is the recommendation from Amazon based on the reading behavior from other readers. Both of those systems are based on algorithms that “learn” from past data.
A recommendation system outdoes benchmarking because it does not need an analyst at the end. It reduces Big Data to small data (For me, small data is also important). A recommendation system suggests a few data points out of a large pool of data. Take LinkedIn as an example: The data product “people you may know” recommends only a few members out of a database of 300 million members.
Thus, recommendation engines are becoming more and more important. Learn from Mr. Ravi Nair the foundations, the theory, see some demos and apply them to your portfolio.
Currently working on big data ecosystem and as the CTO of Percipient, Ravi's expertise and know how will make you enough knowledgeable about machine learning. All are welcome.
5:30-6:15 Socializing, pizza, and beer
6:30-7:30 Collaborative filtering and recommendations in Big Data with Ravi Nair
7:30-8:00 Questions and Closing Remarks.
As always, we'll have a laid back atmosphere with fun, food and beer, and great people! If you have any trouble finding us or have any questions, call Katie at[masked]