SF Data Mining April 2015 - Fully Real-Time Recommendations
Tuesday, April 28, 2015
The SF Data Mining meetup focuses on all aspects of the data pipeline--from data acquisition and big data storage to machine learning and data visualization.


Fully real-time recommendations

Ted Dunning View Bio

Most recommendation algorithms are inherently batch oriented and require all relevant history to be processed. In some contexts such as music, this does not cause significant problems because waiting a day or three before recommendations are available for new items doesn’t significantly change their impact. In other contexts, the value of items drops precipitously with time so that recommending day-old items has little value to users. In this talk, Ted will describe how a large-scale, multi-modal, co-occurrence recommender can be extended to include near real-time updates. In addition, he will show how these real-time updates are compatible with delivery of recommendations via search engines.


Ted Dunning

Ted Dunning is Chief Application Architect at MapR Technologies and committer and PMC member of the Apache Mahout, Apache ZooKeeper, and Apache Drill projects​. Ted has been very active in mentoring new Apache projects and is currently serving as vice president of incubation for the Apache Software Foundation​.​ Ted was the chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems. He built fraud detection systems for ID Analytics (later purchased by LifeLock) and he has 24 patents issued to date and a dozen pending. Ted has a PhD in computing science from the University of Sheffield. When he’s not doing data science, he plays guitar and mandolin. He also bought the beer at the first Hadoop user group meeting..