Building an Online-Recommendation Engine with MongoDB

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Personalized recommendations drive business, helping people find the products they want, the news they need, and the music they didn't know they would love. Despite the obvious advantages, many companies either don't have recommendations or don't leverage their data to make good ones. Too many recommendation engines are black-box algorithms that are hard to change or don't scale well. Using the same recommendation techniques as used at StubHub, Viacom, and AP, this technical webinar will show you how to load your data from MongoDB into Hadoop, generate recommendations, and then put those recommendations into MongoDB, ready to serve end-users. This webinar will prepare you to build a custom recommender for your company that is highly scalable, easy to understand, and built on open-source technology. K Young: About the speaker K Young is the CEO of Mortar Data. Mortar serves data scientists and engineers with a service that makes creating and operating high-scale data pipelines easy. Mortar contributes to several open source projects including Pig, Luigi, and the Mongo-Hadoop connector. Prior to founding Mortar Data, K built software that reaches one in ten public school students in the U.S. He holds a Computer Science degree from Rice University.

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Recommendations are used through out the online world to recommend to a user other products or service they may be interested in. For example, an e commerce site may recommend other products for sale, or an online entertainment site, like Hulu or NetFlicks, may offer entertainment recommendations. This presentation discusses a very elementary recommendation engine implemented in Aerospike and MongoDB

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Squire is an app for iPad that provides suggestions of movies and TV shows, based on the user’s preferences. There are also some social features, for instance a user can suggest a movie or TV show to one of his/her contacts. We have developed the backend and web services that support the Squire iPad app. The recommendations system was written in Scala and everything else was developed in Ruby. This architecture tries to combine the best features of the different languages. The Ruby API is easy to maintain, while the recommendations engine written in Scala and Neo4j is powerful, fast and able to handle heavy computations in a reasonable time.

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