LinkedIn Recommender System Predictive ML vs LLMs

LinkedIn Recommender System Predictive ML vs LLMs

Author: Demetrios August 12, 2025 Duration: 47:39

Demetrios chats with Arpita Vats about how LLMs are shaking up recommender systems. Instead of relying on hand-crafted features and rigid user clusters, LLMs can read between the lines—spotting patterns in user behavior and content like a human would. They cover the perks (less manual setup, smarter insights) and the pain points (latency, high costs), plus how mixing models might be the sweet spot. From timing content perfectly to knowing when traditional methods still win, this episode pulls back the curtain on the future of recommendations.


// Bio

Arpita Vats is a passionate and accomplished researcher in the field of Artificial Intelligence, with a focus on Natural Language Processing, Recommender Systems, and Multimodal AI. With a strong academic foundation and hands-on experience at leading tech companies such as LinkedIn, Meta, and Staples, Arpita has contributed to cutting-edge projects spanning large language models (LLMs), privacy-aware AI, and video content understanding.

She has published impactful research at premier venues and actively serves as a reviewer for top-tier conferences like CVPR, ICLR, and KDD. Arpita’s work bridges academic innovation with industry-scale deployment, making her a sought-after collaborator in the AI research community.

Currently, she is engaged in exploring the alignment and safety of language models, developing robust metrics like the Alignment Quality Index (AQI), and optimizing model behavior across diverse input domains. Her dedication to advancing ethical and scalable AI is reflected both in her academic pursuits and professional contributions.


// Related Links

#recommendersystems #LLMs #linkedin


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Connect with Arpita on LinkedIn: /arpita-v-0a14a422/


Timestamps:

[00:00] Smarter Content Recommendations

[05:19] LLMs: Next-Gen Recommendations

[09:37] Judging LLM Suggestions

[11:38] Old vs New Recommenders

[14:11] Why LLMs Get Stuck

[16:52] When Old Models Win

[22:39] After-Booking Rec Magic

[23:26] One LLM to Rule Models

[29:14] Personalization That Evolves

[32:39] SIM Beats Transformers in QA

[35:35] Agents Writing Research Papers

[37:12] Big-Company Agent Failures

[41:47] LinkedIn Posts Fade Faster

[46:04] Clustering Shifts Social Feeds

[47:01] Vanishing Posts, Replay Mode


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
Author: Language: en-us Episodes: 100

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