Denoising Diffusion Recommender Model - Lecture 2.2
Explore cutting-edge denoising diffusion techniques for recommender systems, enhancing prediction accuracy and user experience in personalized recommendations.
Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity
Explore collaborative filtering using diffusion models, unveiling high-order connectivity potential in recommendation systems for enhanced user experiences.
Diffusion Models for Generative Outfit Recommendation - Session T2.2
Explore innovative diffusion models for generative outfit recommendations, enhancing personalized fashion suggestions through advanced machine learning techniques.
On Generative Agents in Recommendation - Tutorial 2.5
Explore generative agents in recommendation systems, focusing on prompts, instructions, and LLMs to enhance user experiences and personalization.
Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning
Explore LLM-empowered recommendation systems through tool learning, enhancing personalized suggestions and user experiences in information retrieval.
LLaRA: Large Language-Recommendation Assistant
Explore LLaRA, a novel approach integrating large language models with recommender systems for enhanced personalization and user interaction.
Lightweight Embeddings for Graph Collaborative Filtering - SIGIR 2024
Explore lightweight embeddings for graph collaborative filtering in recommender systems, enhancing efficiency and performance in user-item interactions.
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems
Explore TransGNN's fusion of Transformers and Graph Neural Networks for enhanced recommender systems, improving collaborative filtering and user-item interactions.
Content-based Graph Reconstruction for Cold-start Item Recommendation - SIGIR 2024
Explore innovative graph reconstruction techniques for cold-start item recommendations, enhancing content-based approaches in recommender systems.
Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative Filtering
Explore individuality and collectivity of intents in graph collaborative filtering for enhanced recommendation systems and user interaction analysis.
AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Explore adaptive feature de-correlation in graph collaborative filtering for enhanced recommendation systems. Learn innovative techniques to improve accuracy and relevance.
UniSAR: Modeling User Transition Behaviors between Search and Recommendation - Session M3.6
Explore user transition behaviors between search and recommendation systems with UniSAR modeling, enhancing understanding of cross-platform user interactions.
To Search or to Recommend: Predicting Open-App Motivation with Neural Hawkes Process
Explore predicting user app-opening motivations using Neural Hawkes Process, enhancing search and recommendation systems for improved user experiences.
Explainability for Transparent Conversational Information-Seeking
Explore transparent conversational information-seeking through explainability techniques, enhancing user trust and understanding in AI-driven search and recommendation systems.
Sequential Recommendation with Collaborative Explanation via Mutual Information Maximization - Lecture 1
Explore sequential recommendation with collaborative explanation, maximizing mutual information for enhanced explainability in search and recommendation systems.
Modeling User Fatigue for Sequential Recommendation - Session M3.6
Explore innovative approaches to modeling user fatigue in sequential recommendation systems, enhancing user experience and engagement in digital platforms.
MIRROR: A Multi-View Reciprocal Recommender System for Online Recruitment - Domain Specific RecSys
Explore a multi-view reciprocal recommender system for online recruitment, enhancing job matching efficiency and candidate-employer satisfaction.
Multimodality Invariant Learning for Multimedia-Based New Item Recommendation - Lecture 6
Explore innovative techniques for multimedia-based new item recommendation using multimodality invariant learning, enhancing recommendation systems' performance and user experience.
NFARec: A Negative Feedback-Aware Recommender Model - Recommendation Systems
Explore a novel recommender model that incorporates negative feedback for improved personalized recommendations in complex user-item interaction scenarios.
DHMAE: A Disentangled Hypergraph Masked Autoencoder for Group Recommendation - Lecture 4
Explore a novel approach to group recommendation using disentangled hypergraph masked autoencoders, enhancing accuracy and efficiency in collaborative filtering systems.
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.