Association for Computing Machinery (ACM) Courses

What Matters in a Measure? A Perspective from Large-Scale Search Evaluation

Explore key aspects of large-scale search evaluation, focusing on crucial measurement factors and their impact on assessing search engine performance.

• YouTube
• 16 minutes
• On-Demand
• Free Video

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.

• YouTube
• 15 minutes
• On-Demand
• Free Video

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.

• YouTube
• 16 minutes
• On-Demand
• Free Video

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.

• YouTube
• 15 minutes
• On-Demand
• Free Video

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.

• YouTube
• 14 minutes
• On-Demand
• Free Video

Uncontextualized Significance Considered Dangerous in Evaluation - Session M1.5

Explore the dangers of uncontextualized significance in evaluation, presented by Nicola Ferro and Mark Sanderson at SIGIR 2024.

• YouTube
• 15 minutes
• On-Demand
• Free Video

The Treatment of Ties in Rank-Biased Overlap - Evaluation M1.5

Explore the treatment of ties in Rank-Biased Overlap, examining its impact on information retrieval evaluation metrics and ranking systems.

• YouTube
• 15 minutes
• On-Demand
• Free Video

Fine-grained Textual Inversion Network for Zero-Shot Composed Image Retrieval - Multimedia 1

Explore advanced techniques for zero-shot composed image retrieval using fine-grained textual inversion networks in this cutting-edge research presentation.

• YouTube
• 13 minutes
• On-Demand
• Free Video

COMI: Correct and Mitigate Shortcut Learning Behavior in Deep Neural Networks

Explore techniques to correct and mitigate shortcut learning in deep neural networks, enhancing model robustness and generalization capabilities.

• YouTube
• 13 minutes
• On-Demand
• Free Video

Revisiting Document Expansion and Filtering for Effective First-Stage Retrieval - Lecture 3

Explore innovative techniques for enhancing first-stage retrieval efficiency through document expansion and filtering, improving search engine performance.

• YouTube
• 16 minutes
• On-Demand
• Free Video

Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images - Multimedia 1

Explore text-image retrieval models' bias towards AI-generated images and its implications for multimedia search and content creation.

• YouTube
• 12 minutes
• On-Demand
• Free Video

Neural Passage Quality Estimation for Static Pruning - Efficiency for Search

Explore neural passage quality estimation for static pruning in search efficiency, enhancing information retrieval systems.

• YouTube
• 14 minutes
• On-Demand
• Free Video

Unsupervised Cross-Domain Image Retrieval with Semantic-Attended Mixture-of-Experts

Explore unsupervised cross-domain image retrieval using semantic-attended mixture-of-experts, enhancing retrieval accuracy across diverse visual domains.

• YouTube
• 11 minutes
• On-Demand
• Free Video

GUITAR: Gradient Pruning toward Fast Neural Ranking - Efficiency for Search

Explore gradient pruning techniques for optimizing neural ranking models, enhancing search efficiency and performance.

• YouTube
• 11 minutes
• On-Demand
• Free Video

Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations

Explore efficient inverted indexes for approximate retrieval using learned sparse representations, enhancing search performance and accuracy.

• YouTube
• 15 minutes
• On-Demand
• Free Video