Leveraging Predictive Models and Data Science to Optimize Information Retrieval Systems

Abstract: 

This presentation explores how data science and predictive modeling optimize the performance and scalability of information retrieval (IR) systems. We'll examine the impact of query analysis, document ranking, and result aggregation on user satisfaction. Our research demonstrates that techniques like keyword extraction, intent analysis, and custom deep ranking models can reduce irrelevant results by up to 26% while decreasing computing costs by more than 39%. We'll address the challenges of scaling IR systems to handle massive datasets and high query volumes, highlighting how predictive models streamline resource-intensive processes. Finally, we'll present optimization strategies leveraging distributed computing, multi-stage caching, and predictive ranking models to enhance throughput, reduce latency, and minimize computational overhead. This presentation offers valuable insights for those interested in the intersection of data science and information retrieval.

Bio: 

Hareen is an accomplished AI leader with over 12 years of experience driving innovation in machine learning and search technologies. With a Master's in Computer Science, Hareen has made significant contributions at industry giants like Uber, Apple, Amazon, and eBay. Hareen's expertise in areas such as recommendation systems, natural language processing, and deep learning has directly impacted billions of users worldwide and generated millions in revenue. Hareen possesses a proven track record of building high-performance teams, developing cutting-edge solutions, and delivering tangible business results.

Open Data Science

 

 

 

Open Data Science
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