Sign up to save your podcasts
Or
Share TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest
In this episode, we delve into the paper "TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest" . This research introduces TransAct, a novel Transformer-based model designed to enhance Pinterest's recommendation system by capturing users' short-term preferences through their real-time activities.
Research Paper Link - arxiv.org+4arxiv.org+4export.arxiv.org+4
🔹 What’s Inside?
- Hybrid Ranking Approach – Combines real-time user behavior with long-term embeddings for better recommendations.
- Production Deployment – Powers multiple Pinterest surfaces like Homefeed, Search, and Notifications.
- Proven Impact – A/B tests show improved recommendation quality and engagement.
Tune in to learn how TransAct balances real-time responsiveness with efficiency in large-scale AI-driven personalization. 🚀
View all episodes
By Sunil & JitenIn this episode, we delve into the paper "TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest" . This research introduces TransAct, a novel Transformer-based model designed to enhance Pinterest's recommendation system by capturing users' short-term preferences through their real-time activities.
Research Paper Link - arxiv.org+4arxiv.org+4export.arxiv.org+4
🔹 What’s Inside?
- Hybrid Ranking Approach – Combines real-time user behavior with long-term embeddings for better recommendations.
- Production Deployment – Powers multiple Pinterest surfaces like Homefeed, Search, and Notifications.
- Proven Impact – A/B tests show improved recommendation quality and engagement.
Tune in to learn how TransAct balances real-time responsiveness with efficiency in large-scale AI-driven personalization. 🚀