Probabilistic Topic Models and User Behavior
Explore latent thematic structures in document collections and user behavior data. Learn how topic modeling algorithms can enhance predictions and understanding of collection organization.
Towards Intelligent Fashion Recommendation
Explore intelligent fashion recommendation systems, covering research topics, criteria, databases, and future developments in AI-driven style suggestions.
Adaptive Fair Representation Learning for Personalized Fairness in Recommendations - Lecture 7
Explore adaptive fair representation learning for personalized fairness in recommendations through information alignment techniques.
EditKG: Editing Knowledge Graph for Recommendation - Lecture 1
Explore innovative techniques for editing knowledge graphs to enhance recommendation systems, focusing on improving accuracy and relevance.
Large Language Models for Intent-Driven Session Recommendations - Session M1.6
Explore intent-driven session recommendations using large language models for enhanced personalized content delivery in recommender systems.
Sequential Recommendation with Latent Relations Based on Large Language Models - M1.6
Explore advanced sequential recommendation techniques using large language models to uncover latent relations in user behavior patterns.
Supercharging Search with Language Models (LLMs)
Explore how Instacart revolutionized search using Language Models, enhancing user experience with personalized recommendations and improved results.
Shopping Recommendations at Pinterest
Explore Pinterest's shopping recommendation systems, including engagement-based and embedding-based candidate generation, indexing methods, and deep neural network ranking models.
How to Iterate and Productionize Machine Learning Models with Speed
Explore strategies to overcome deployment challenges and accelerate the productionization of machine learning models for faster impact.
Lessons Learned: The Journey to Real-Time Machine Learning at Instacart
Explore Instacart's transition to real-time machine learning, focusing on infrastructure, use cases, challenges, and key lessons for implementing ML in a dynamic marketplace.
Shopping Recommendations at Pinterest
Explore Pinterest's shopping recommendation systems, from candidate generation to serving methods for diverse recommenders and neural network ranking models.
An Empirical Analysis on Multi-turn Conversational Recommender Systems - Lecture 3.2
Explore multi-turn conversational recommender systems through empirical analysis, examining their effectiveness and potential improvements in interactive recommendation scenarios.
Towards Human-centered Proactive Conversational Agents - Conversational IR and Recommendation
Explore human-centered proactive conversational agents in IR and recommendation systems, focusing on advancements and challenges in creating more intuitive AI interactions.
SIGformer: Sign-aware Graph Transformer for Recommendation
Explore sign-aware graph transformers for recommendation systems, enhancing accuracy through innovative graph-based techniques and transformer architectures.
Adaptive In-Context Learning with Large Language Models for Bundle Generation - Recommendation Systems
Explore adaptive in-context learning techniques for bundle generation using large language models in recommendation systems.
Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendation
Explore advanced techniques in multi-behavior recommendation systems, focusing on behavior-contextualized item preference modeling for improved personalization and accuracy.
MDMTRec: An Adaptive Multi-Task Multi-Domain Recommendation Framework - SIGIR 2024
Explore an adaptive framework for multi-task, multi-domain recommendations, enhancing personalized content suggestions across various platforms and contexts.
Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation - Session M3.5
Explore critical insights into beyond-accuracy performance metrics in next basket recommendation systems, challenging current assumptions and methodologies.
GPT-4 Recommendation Engine - OpenAI Tool Calling for Clothing Suggestions
Explore building a recommendation engine using OpenAI's API to suggest clothing items based on user inputs and contextual information, leveraging the gpt-4o-mini model for personalized product searches.
Coding TensorFlow
Comprehensive series exploring TensorFlow's capabilities, from basics to advanced topics like NLP, computer vision, and recommendation systems. Hands-on coding tutorials for various ML applications.
Building Recommendation Systems with TensorFlow
Learn to create powerful recommendation systems using TensorFlow, covering content-based and collaborative filtering, ranking models, multitask learning, and efficient serving with ScaNN.