Navigating the Complexity of Recommendation Systems: Techniques and Challenges

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In the era of information overload, recommendation systems have emerged as essential tools for filtering and personalizing content, thereby enhancing user experience across various digital platforms. These systems employ sophisticated algorithms to analyze user preferences, behaviors, and interactions to suggest items ranging from products and movies to news articles and academic papers. This talk aims to demystify the underlying mechanisms of recommendation systems, highlighting key techniques such as collaborative filtering, content-based filtering, and hybrid approaches. We will explore the challenges faced in developing effective recommendation systems, including issues of data sparsity, scalability, cold start, and ensuring diversity and fairness in recommendations. Furthermore, the talk will delve into the ethical considerations and the impact of bias in recommendation algorithms. By providing a comprehensive overview of recommendation systems, this talk seeks to inspire future research and development efforts aimed at creating more intelligent, transparent, and user-centric solutions.

Agenda:

(PDT) 11:45 am - 11:50 am Arrival, socializing, and Opening
(PDT) 11:50 am - 1:45 pm Khojasteh Mirza "Navigating the Complexity of Recommendation Systems: Techniques and Challenges"
(PDT) 1:45 pm - 1:50 pm Q&A

About Khojasteh Mirza

Khojasteh Mirza is a Master of Engineering student at Cornell Tech, specializing in Electrical and Computer Engineering with a focus on Data Science and Applied Machine Learning. With a rich academic background, including a Bachelor of Technology in Mechatronics Engineering from NMIMS University, Khojasteh has excelled academically, securing a Merit Scholarship and achieving high honors. His technical skills encompass a wide array of data science technologies, and his professional experience includes impactful roles as a Project Research Assistant at the Indian Institute of Technology Bombay, a Data Analyst at AIRMO Gmbh and Ozibook and most recently, responsible for developing recommendation systems for Cognitii, a learning app for neurodivergent individuals. Khojasteh's work spans innovative projects in healthcare industry developing predictive models for Parkinson’s disease in collaboration with Tata Consultancy Servicesdemonstrating his ability to apply data science for real-world impact.

https://us02web.zoom.us/webinar/register/5117080164625/WN_EPMw1tZGTD-SxWkSxJpBLg

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