Founder @ Searchkernel, author of
AI-Powered Search and Solr in Action, startup Advisor, researcher/ public speaker on search, relevance & ranking, recommendation systems, and natural language processing.
Paper Abstract: As the ability to store and process massive amounts of user behavioral data increases, new approaches continue to arise for leveraging the wisdom of the crowds to gain insights that were previously very challenging to discover by text mining alone. For example, through collaborative filtering, we can learn previously hidden relationships between items […]
My team was fortunate to have 2 papers accepted for publication through the 2014 IEEE International Conference on Big Data, held last week in Washington, D.C. I presented one of the papers titled “Crowdsourced Query Augmentation through the Semantic Discovery of Domain-specific Jargon.” The slides and video (coming soon) are posted below for anyone who […]
Paper Abstract: In the big data era, scalability has become a crucial requirement for any useful computational model. Probabilistic graphical models are very useful for mining and discovering data insights, but they are not scalable enough to be suitable for big data problems. Bayesian Networks particularly demonstrate this limitation when their data is represented using […]
Paper Abstract: Common difficulties like the cold-start problem and a lack of sufficient information about users due to their limited interactions have been major challenges for most recommender systems (RS). To overcome these challenges and many similar ones that result in low accuracy (precision and recall) recommendations, we propose a novel system that extracts semantically-related […]
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