Data Skeptic Networks and Recommender Systems
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Aug 17, 2025 The hosts introduce an exciting new season focused on recommender systems. They dive into how network science aids in shaping recommendation algorithms, highlighting the significance of both explicit and implicit connections. Discussions on the Internet of Agents reveal the potential of AI agents in collaborative ecosystems. They also tackle link prediction and node similarity, sharing personal experiences with recommender systems and challenges like the cold start problem. Lastly, insights into influencer dynamics using PageRank add a fresh perspective on social influence.
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Call For Practitioner Interviews
- If you work on recommender systems, contact DataSkeptic to share hands-on industry use cases via rs@dataskeptic.com.
- Kyle explicitly requests interviews with technical practitioners, not executives, for practical conversations.
Industry Drives Recommender Advances
- Cutting-edge recommender work and resources often live in industry rather than academia.
- Asaf highlights practitioners bring insights from real-world deployments and data constraints.
Recommendations As Implicit Link Prediction
- Recommender systems infer implicit edges between items or users, unlike explicit network ties.
- Asaf frames recommendations as link-prediction of unseen connections in a user's mind.
