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Found 7 papers, 1 papers with code
Aligning Large Language Models with Recommendation Knowledge
Operations such as Masked Item Modeling (MIM) and Bayesian Personalized Ranking (BPR) have found success in conventional recommender systems.
Online Matching: A Real-time Bandit System for Large-scale Recommendations
In this paper, we introduce Online Matching: a scalable closed-loop bandit system learning from users' direct feedback on items in real time.
Better Generalization with Semantic IDs: A case study in Ranking for Recommendations
We showcase how we use them as a replacement of item IDs in a resource-constrained ranking model used in an industrial-scale video sharing platform.
Long-Term Value of Exploration: Measurements, Findings and Algorithms
We conduct live experiments on one of the largest short-form video recommendation platforms that serves billions of users to validate the new experiment designs, quantify the long-term values of exploration, and to verify the effectiveness of the adopted neural linear bandit algorithm for exploration.
Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations
We reconcile these notions and show that the tension is due to differences in distributions of users where items are relevant, and break down the important factors of the user's recommendations.
Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations
However, batch loss is subject to sampling bias which could severely restrict model performance, particularly in the case of power-law distribution.
Fairness in Recommendation Ranking through Pairwise Comparisons
Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information.
