Axiomatic Causal Interventions for Reverse Engineering Relevance Computation in Neural Retrieval Models - Lecture 3
Explore axiomatic causal interventions for reverse engineering relevance computation in neural retrieval models, enhancing understanding of information retrieval systems.
Multi-granular Adversarial Attacks Against Black-box Neural Ranking Models - Lecture 1
Explore multi-granular adversarial attacks on black-box neural ranking models in information retrieval, examining vulnerabilities and potential defense strategies.
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.
A Taxation Perspective for Fair Re-ranking - SIGIR 2024
Explore a taxation perspective for fair re-ranking in information retrieval, examining fairness considerations and algorithmic approaches.
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.
Dimension Importance Estimation for Dense Information Retrieval - Tutorial 2.1
Explore dimension importance estimation techniques for dense information retrieval, enhancing search accuracy and efficiency in large-scale 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.
Deep Automated Mechanism Design for Integrating Ad Auction and Allocation in Feed - T1.3
Explore deep automated mechanism design for integrating ad auction and allocation in feed, focusing on CTR, ads, and click models.
Leveraging LLMs for Unsupervised Dense Retriever Ranking - Dense Retrieval 1
Explore unsupervised dense retriever ranking using LLMs for improved information retrieval techniques in this cutting-edge research presentation.
Counterfactual Ranking Evaluation with Flexible Click Models - Lecture 3
Explore counterfactual ranking evaluation techniques using flexible click models for improved CTR and ad performance in search systems.
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.
Graded Relevance Scoring of Written Essays with Dense Retrieval - Tutorial 2.1
Explore dense retrieval techniques for graded relevance scoring of written essays, enhancing automated assessment in educational contexts.
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.
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.
Enhancing Dataset Search with Compact Data Snippets - SIGIR 2024
Explore techniques for improving dataset search using compact data snippets, enhancing discoverability and accessibility of valuable information resources.