Mastering Recommender Systems
Explore top strategies for building effective e-commerce recommendation systems, featuring insights from Kaggle Grandmasters on candidate generation, reranking, and model ensembling techniques.
A Recommender Network Perspective on the Informational Value of Critics and Crowds
Explore the informational value of critics and crowds through a recommender network lens, offering insights into decision-making and information processing in complex systems.
Configurable Fairness for New Item Recommendation Considering Entry Time - SIGIR 2024
Explore configurable fairness in recommender systems, focusing on new item recommendations and entry time considerations for enhanced algorithmic equity.
Fair Recommendations with Limited Sensitive Attributes - A Distributionally Robust Optimization Approach
Explore distributionally robust optimization for fair recommendations with limited sensitive attributes in RecSys, enhancing fairness in machine learning applications.
Going Beyond Popularity and Positivity Bias: Correcting Multifactorial Bias in Recommender Systems - M1.7
Explore techniques for addressing multiple biases in recommender systems, focusing on fairness and accuracy beyond popularity and positivity.
CaDRec: Contextualized and Debiased Recommender Model - Fairness in RecSys
Explore a contextualized and debiased recommender model for fair and accurate recommendations in information retrieval systems.
Reinforcement Learning-based Recommender Systems with Large Language Models - SIGIR 2024
Explore reinforcement learning-based recommender systems using LLMs for state reward and action modeling in this cutting-edge research presentation.
Fair Sequential Recommendation without User Demographics - SIGIR 2024
Explore fair sequential recommendation techniques without relying on user demographics, enhancing ethical AI practices in recommender systems.
Data-efficient Fine-tuning for LLM-based Recommendation - SIGIR 2024 M1.6
Explore data-efficient fine-tuning techniques for LLM-based recommendation systems, enhancing performance with minimal data requirements.
Can We Trust Recommender System Fairness Evaluation? The Role of Fairness and Relevance - Evaluation M1.5
Explore the reliability of fairness evaluations in recommender systems, examining the interplay between fairness and relevance metrics for more trustworthy assessments.
Enhancing Sequential Recommenders with Augmented Knowledge from Aligned LLMs - SIGIR 2024
Explore innovative techniques for enhancing sequential recommender systems using aligned large language models, improving personalized recommendations in various applications.
IDGenRec: LLM-RecSys Alignment with Textual ID Learning - Lecture 1
Explore LLM-RecSys alignment through textual ID learning, enhancing recommendation systems with advanced language models.
Building Scalable ML Services for Rapid Development in Health and Wellness
Discover how to build and scale ML services for health and wellness products, reducing release time by 85% using open-source tools like DBT, AirFlow, and MLFlow.
Machine Learning on Dynamic Graphs - Temporal Graph Networks
Explore Temporal Graph Networks for dynamic graph analysis, unlocking insights in evolving systems like social networks and financial transactions.
Supercharging Recommender Systems - Unleashing the Power of Distributed Model Training
Explore distributed model training techniques to enhance recommender systems, boosting performance and scalability for production environments.
Continuously Improving Large Scale Recommenders with MLOps Tools and Practices
Leverage MLOps tools and practices to operationalize and continuously improve recommendation systems at scale using NVIDIA Merlin and Kubeflow on Google Kubernetes Engine.
Testing Recommender Systems in the Wild - MLOps World
Explore behavioral testing for recommender systems using RecList. Scale up real-world testing in research and production with hands-on coding and practical examples.
GPT4Rec: Graph Prompt Tuning for Streaming Recommendation - Tutorial 2.5
Explore graph prompt tuning for streaming recommendations using GPT4Rec, enhancing real-time personalized content delivery in dynamic environments.
Large Language Models for Next Point-of-Interest Recommendation - Tutorial 2.4
Explore innovative applications of large language models in next point-of-interest recommendation systems for enhanced user experiences and improved location-based services.
Disentangled Contrastive Hypergraph Learning for Next POI Recommendation - Lecture 4
Explore innovative hypergraph learning techniques for next POI recommendations, enhancing user experience in location-based services.
Graph Signal Diffusion Model for Collaborative Filtering - Tutorial 2.2
Explore graph signal diffusion models for collaborative filtering in recommendation systems, enhancing predictive accuracy and user experience.