• Recommender Systems with Carey Morewedge

  • 2024/10/23
  • 再生時間： 59 分
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Recommender Systems with Carey Morewedge

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  • In this episode of the Behavioral Design Podcast, we delve into the world of AI recommender systems with special guest Carey Morewedge, a leading expert in behavioral science and AI. The discussion covers the fundamental mechanics behind AI recommendation systems, including content-based filtering, collaborative filtering, and hybrid models. Carey explains how platforms like Netflix, Twitter, and TikTok use implicit data to make predictions about user preferences, and how these systems often prioritize short-term engagement over long-term satisfaction. The episode also touches on ethical concerns, such as the gap between revealed and normative preferences, and the risks of relying too much on algorithms without considering the full context of human behavior. Join co-hosts Aline Holzwarth and Samuel Salzer as they together with Carey explore the delicate balance between human preferences and algorithmic influence. This episode is a must-listen for anyone interested in understanding the complexities of AI-driven recommendations! -- LINKS: Carey Morewedge: Google Scholar Profile Carey Morewedge - LinkedIn Boston University Faculty Page Personal Website Understanding AI Recommender Systems: How Netflix’s Recommendation System WorksImplicit Feedback for Recommender Systems (Research Paper)Why People Don’t Trust Algorithms (Harvard Business Review)⁠Nuance Behavior Website⁠ -- TIMESTAMPS: 00:00 The 'Do But Not Recommend' Game 07:53 The Complexity of Recommender Systems 08:58 Types of Recommender Systems 12:08 Introducing Carey Morewedge 14:13 Understanding Decision Making in AI 17:00 Challenges in AI Recommendations 32:13 Long-Term Impact on User Behavior 33:00 Understanding User Preferences 35:03 Challenges with A/B Testing 40:06 Algorithm Aversion 46:51 Quickfire Round: To AI or Not to AI 52:55 The Future of AI and Human Relationships -- Interesting in collaborating with Nuance? If you’d like to become one of our special projects, email us at hello@nuancebehavior.com or book a call directly on our website: ⁠nuancebehavior.com.⁠ Support the podcast by joining ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Habit Weekly Pro⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ 🚀. Members get access to extensive content databases, calls with field leaders, exclusive offers and discounts, and so much more. Every Monday our ⁠⁠⁠⁠⁠Habit Weekly newsletter⁠⁠⁠⁠⁠ shares the best articles, videos, podcasts, and exclusive premium content from the world of behavioral science and business. Get in touch via ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠podcast@habitweekly.com⁠⁠⁠⁠⁠ The song used is ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Murgatroyd by David Pizarro⁠
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In this episode of the Behavioral Design Podcast, we delve into the world of AI recommender systems with special guest Carey Morewedge, a leading expert in behavioral science and AI. The discussion covers the fundamental mechanics behind AI recommendation systems, including content-based filtering, collaborative filtering, and hybrid models. Carey explains how platforms like Netflix, Twitter, and TikTok use implicit data to make predictions about user preferences, and how these systems often prioritize short-term engagement over long-term satisfaction. The episode also touches on ethical concerns, such as the gap between revealed and normative preferences, and the risks of relying too much on algorithms without considering the full context of human behavior. Join co-hosts Aline Holzwarth and Samuel Salzer as they together with Carey explore the delicate balance between human preferences and algorithmic influence. This episode is a must-listen for anyone interested in understanding the complexities of AI-driven recommendations! -- LINKS: Carey Morewedge: Google Scholar Profile Carey Morewedge - LinkedIn Boston University Faculty Page Personal Website Understanding AI Recommender Systems: How Netflix’s Recommendation System WorksImplicit Feedback for Recommender Systems (Research Paper)Why People Don’t Trust Algorithms (Harvard Business Review)⁠Nuance Behavior Website⁠ -- TIMESTAMPS: 00:00 The 'Do But Not Recommend' Game 07:53 The Complexity of Recommender Systems 08:58 Types of Recommender Systems 12:08 Introducing Carey Morewedge 14:13 Understanding Decision Making in AI 17:00 Challenges in AI Recommendations 32:13 Long-Term Impact on User Behavior 33:00 Understanding User Preferences 35:03 Challenges with A/B Testing 40:06 Algorithm Aversion 46:51 Quickfire Round: To AI or Not to AI 52:55 The Future of AI and Human Relationships -- Interesting in collaborating with Nuance? If you’d like to become one of our special projects, email us at hello@nuancebehavior.com or book a call directly on our website: ⁠nuancebehavior.com.⁠ Support the podcast by joining ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Habit Weekly Pro⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ 🚀. Members get access to extensive content databases, calls with field leaders, exclusive offers and discounts, and so much more. Every Monday our ⁠⁠⁠⁠⁠Habit Weekly newsletter⁠⁠⁠⁠⁠ shares the best articles, videos, podcasts, and exclusive premium content from the world of behavioral science and business. Get in touch via ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠podcast@habitweekly.com⁠⁠⁠⁠⁠ The song used is ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Murgatroyd by David Pizarro⁠
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