Picture for Lukasz Heldt

Lukasz Heldt

Aligning Large Language Models with Recommendation Knowledge

Add code
Mar 30, 2024
Viaarxiv icon

Online Matching: A Real-time Bandit System for Large-scale Recommendations

Add code
Jul 29, 2023
Figure 1 for Online Matching: A Real-time Bandit System for Large-scale Recommendations
Figure 2 for Online Matching: A Real-time Bandit System for Large-scale Recommendations
Figure 3 for Online Matching: A Real-time Bandit System for Large-scale Recommendations
Figure 4 for Online Matching: A Real-time Bandit System for Large-scale Recommendations
Viaarxiv icon

Better Generalization with Semantic IDs: A case study in Ranking for Recommendations

Add code
Jun 13, 2023
Figure 1 for Better Generalization with Semantic IDs: A case study in Ranking for Recommendations
Figure 2 for Better Generalization with Semantic IDs: A case study in Ranking for Recommendations
Figure 3 for Better Generalization with Semantic IDs: A case study in Ranking for Recommendations
Figure 4 for Better Generalization with Semantic IDs: A case study in Ranking for Recommendations
Viaarxiv icon

Value of Exploration: Measurements, Findings and Algorithms

Add code
May 12, 2023
Figure 1 for Value of Exploration: Measurements, Findings and Algorithms
Figure 2 for Value of Exploration: Measurements, Findings and Algorithms
Figure 3 for Value of Exploration: Measurements, Findings and Algorithms
Figure 4 for Value of Exploration: Measurements, Findings and Algorithms
Viaarxiv icon

Recommender Systems with Generative Retrieval

Add code
May 08, 2023
Figure 1 for Recommender Systems with Generative Retrieval
Figure 2 for Recommender Systems with Generative Retrieval
Figure 3 for Recommender Systems with Generative Retrieval
Figure 4 for Recommender Systems with Generative Retrieval
Viaarxiv icon

Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations

Add code
Oct 14, 2022
Figure 1 for Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations
Figure 2 for Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations
Figure 3 for Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations
Figure 4 for Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations
Viaarxiv icon

Fairness in Recommendation Ranking through Pairwise Comparisons

Add code
Mar 02, 2019
Figure 1 for Fairness in Recommendation Ranking through Pairwise Comparisons
Figure 2 for Fairness in Recommendation Ranking through Pairwise Comparisons
Figure 3 for Fairness in Recommendation Ranking through Pairwise Comparisons
Figure 4 for Fairness in Recommendation Ranking through Pairwise Comparisons
Viaarxiv icon