@thesamparr Interesting product, but a spell and grammar check would go a long way in building customer trust!
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Amey Dharwadker
68 posts
Amey Dharwadker
@ameydhar
Engineering Manager, Machine Learning @Facebook. Building video recommender systems serving billions of users. Writing on Machine Learning & AI.
San Francisco, California Katılım Temmuz 2021
103 Takip Edilen36 Takipçiler
For the last 3 years, this one guy's been emailing me updates on his business.
Year 1
him: I have this biz idea. mouth tape when you sleep
me: that's dumb
Year 2:
him: we did $2m in revenue
me: wow, people are dumb
year 3
him: we did $14m
me: i'm dumb
Give full break down on this wild story in today's pod.
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Fascinating read on @Google OKR discussion covering search and ads long vs short term metrics, financial goals for search and cannibalization across teams.
Deedy@deedydas
Shashi, one of the engg leads for Google Search, in a leaked internal email says "there's no way I'm signing up for a daily active users goal (OKR)". The focus was always on user value, not growth at all costs. There are SOME companies who would not think twice about it.
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@karpathy How did you ensure that the model hadn’t seen these questions and answers in its training data already?
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CDR-Adapter tackles data sparsity and cold-start in cross-domain recsys, separating the model from the mapping function. It's a plug-and-play module for flexible knowledge transfer and efficient fine-tuning with minimal training costs.
Link: arxiv.org/abs/2311.02398
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ControlRec: Bridging the Semantic Gap between Language Model and Personalized Recommendation
Novel contrastive prompt learning framework that aligns user IDs and natural language features, employing Heterogeneous Feature Matching (HFM) and Instruction Contrastive Learning (ICL).
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Amey Dharwadker retweetledi
Delighted to announce the winners of the 2023 BCS Search Industry Awards. Congratulations to
batteryincluded.ai, Wikiframe Visual Graph, @ameydhar and @FlaxSearch.
Thanks also to our judges @molnaragnes, Andy Hind and Peter Cotroneo
isquared.wordpress.com/2023/11/27/ann…
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Delighted to be a part of this insightful conversation with @jaygshah22, discussing the fascinating world of building and deploying billion-scale video recommender systems, tips on how to get started or transition into AI, etc. Do check out the podcast!
Jay Shah, Ph.D.@jaygshah22
My podcast with @ameydhar on building Video Recommendation systems at @Meta for @facebook-Watch (2 billion+ users), improving user experience and engagement using #MachineLearning & some tips for acing at ML roles in the industry. youtu.be/uCd0x1FYTeM
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@facebook @TheWebConf @FacebookWatch @banarasi_ghat Retweet the first tweet of this thread to spread the word and share these works with your network interested in industrial #RecommenderSystems and #MachineLearning for personalization!
twitter.com/ameydhar/statu…
Amey Dharwadker@ameydhar
🚨 Exciting news! 🚨 Two papers from our @facebook Video Recommendations team have been accepted for publication at ACM @TheWebConf (WWW 2023)! Our #recsys work highlights how we're enhancing video watching experiences for users worldwide. 👇
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@facebook @TheWebConf Both these works have been successfully deployed in production on @FacebookWatch, a popular video discovery and sharing platform serving billions of users.
Huge shoutout to all my amazing collaborators! @banarasi_ghat
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🚨 Exciting news! 🚨 Two papers from our @facebook Video Recommendations team have been accepted for publication at ACM @TheWebConf (WWW 2023)! Our #recsys work highlights how we're enhancing video watching experiences for users worldwide. 👇
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Amey Dharwadker retweetledi
Our two papers have been accepted at @TheWebConf (WWW '23), showcasing outstanding work of my team at @facebook Video Recommendations in enhancing the user's watch experience.
arxiv.org/abs/2304.08435
arxiv.org/abs/2304.06844
#recsys #ml
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We are at the iPhone moment of AI.
It will transform the future of speech, creativity & biology forever.
Here's every AI announcement from Nvidia's GTC event:
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Dynamically Expandable Graph Convolution for Streaming Recommendation
Proposes a novel algorithm for streaming recommendation, disentangling outdated short-term preferences from useful long-term preferences, capturing individual-level preference shifts.
arxiv.org/abs/2303.11700
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Debiased Contrastive Learning for Sequential Recommendation
Proposes a Debiased Contrastive learning paradigm for Recommendation (DCRec) that unifies sequential pattern encoding and collaborative relation modeling to tackle popularity bias.
Paper: arxiv.org/abs/2303.11780
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Amey Dharwadker retweetledi
@PeterDiamandis The ethical implications of AI creating seemingly "real" content raises questions about accountability and control.
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do you have any concerns about generative AI? what are they?
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Two-Stage Constrained Actor-Critic for Short Video Recommendation
Proposes a novel method to optimize short video recommendations, balancing both main goal (cumulative watch time) and auxiliary goals (interactions) using reinforcement learning.
arxiv.org/abs/2302.01680
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