Exponential Reward Weighting for Fine-Tuning Generative Recommenders under Sparse and Noisy Feedback
Proposed Exp-RSFT method uses exponential reward weighting to enhance recommender systems' ranking performance.
Key Findings
Methodology
Exp-RSFT employs exponential weighting of each logged interaction to avoid over-optimizing unreliable reward models. The temperature parameter λ balances exploiting high-reward behavior and robustness to noise.
Key Results
- Across three public benchmarks and an industrial dataset, Exp-RSFT's performance follows an inverted-U trend with λ, significantly outperforming PPO and DPO.
- On the ML-1M dataset, Exp-RSFT achieved an NDCG@10 of 0.1465, compared to PPO's 0.0303.
- On the StreamCo dataset, Exp-RSFT improved NDCG@10 by 106.20% over RSFT.
Significance
This study provides a new perspective on optimizing generative recommenders under sparse and noisy feedback. The exponential weighting method avoids the over-reliance on unreliable reward models, enhancing recommendation quality.
Technical Contribution
Technical contributions include a theoretical framework for exponential reward weighting, analysis of its suboptimality decomposition, and experimental validation across various datasets.
Novelty
This method introduces the concept of exponential reward weighting in recommender systems, significantly improving recommendation quality under sparse and noisy feedback.
Limitations
- The method relies on offline data, lacking online exploration capabilities.
- The choice of temperature parameter λ requires empirical tuning.
Future Work
Future work could explore the application of this method in other types of recommender systems and automate the selection of the temperature parameter.
AI Executive Summary
In recommender systems, user interaction data is often sparse and noisy, posing challenges for optimizing generative recommenders. Traditional methods like PPO and DPO tend to over-optimize unreliable reward models, leading to degraded recommendation quality.
This paper proposes a novel Exponential Reward-Weighted Fine-Tuning (Exp-RSFT) method, which applies exponential weighting to each logged interaction, directly optimizing the logged rewards and avoiding reliance on unreliable reward models. Experimental results demonstrate significant improvements in recommendation performance across multiple datasets.
While the method performs well in experiments, it relies on offline data and lacks online exploration capabilities. Future research could explore how to apply this method in dynamic environments.
Deep Analysis
Background
Recommender systems play a crucial role in modern information retrieval. Traditional methods like PPO and DPO perform poorly with sparse and noisy data, often over-optimizing unreliable reward models.
Core Problem
User interaction data is sparse and noisy, making reward models difficult to generalize. Directly optimizing imperfect rewards can lead to over-optimization issues.
Innovation
Exp-RSFT applies exponential weighting to directly optimize logged rewards, avoiding reliance on unreliable reward models. The temperature parameter λ balances exploiting high-reward behavior and robustness to noise.
Methodology
- �� Use exponential weighting to optimize each logged interaction
- �� Adjust noise impact with temperature parameter λ
- �� Theoretical analysis of suboptimality decomposition, validating experimental results
Experiments
Experiments conducted on ML-1M, ML-20M, Amazon Books, and StreamCo datasets compare Exp-RSFT's performance with PPO, DPO, and other methods.
Results
Exp-RSFT outperformed all other methods across datasets, especially in sparse and noisy datasets, significantly better than traditional methods.
Applications
Applicable to recommender systems dealing with sparse and noisy feedback, particularly in large industrial datasets.
Limitations & Outlook
Relies on offline data, lacking online exploration capabilities. The choice of temperature parameter λ requires empirical tuning.
Plain Language Accessible to non-experts
Imagine a library with countless books. Users can only see a small portion and provide feedback. The recommender system is like a librarian, needing to recommend books based on user feedback. But this feedback might be inaccurate, like users just flipping through a book. The Exp-RSFT method is like a smart librarian, adjusting recommendations based on the strength of user feedback to ensure the recommended books better match the user's true interests.
ELI14 Explained like you're 14
Imagine you're in a huge amusement park, but only a few rides are ones you really like. The recommender system is like a tour guide, suggesting rides based on your feedback. But sometimes, your feedback might be off, like trying a ride just out of curiosity. Exp-RSFT is like a smart tour guide, adjusting recommendations based on your feedback strength to make sure the suggested rides truly match your interests.
Glossary
Recommender System
A system that suggests items to users based on their historical behavior.
Used in this paper to optimize user-item matching.
Generative Recommender
A system that generates sequences of items a user might be interested in.
Used in this paper to simulate user interaction history.
Sparse Feedback
Feedback where users interact with only a small number of items, leading to sparse data.
One of the main challenges faced by recommender systems in this paper.
Noise
Random errors or deviations in data.
Refers to the inaccuracy of user feedback in this paper.
Exponential Weighting
A method of weighting data using an exponential function.
Used in this paper to optimize rewards in recommender systems.
Open Questions Unanswered questions from this research
- 1 How can Exp-RSFT be applied in dynamic environments?
- 2 How to automate the selection of the temperature parameter λ to optimize performance?
Applications
Immediate Applications
Movie Recommendation
Apply Exp-RSFT in movie recommendation systems to enhance user satisfaction.
Long-term Vision
Cross-Domain Recommendation
Explore the potential of Exp-RSFT in various domain recommendation systems.
Abstract
In recommendation systems, users interact with only a small fraction of a vast item catalog, producing feedback that is both sparse and noisy. This challenges post-training generative recommenders: reward models trained from logged interactions often fail to generalize, while directly optimizing imperfect rewards can lead to reward over-optimization. We propose Exponential reward-weighted fine-tuning (Exp-RSFT), where each logged interaction is weighted by $\exp(r/λ)$, avoids this failure by optimizing directly on the logged rewards, with the temperature $λ$ regularizing against their noise. We theoretically show that Exp-RSFT's suboptimality decomposes into two costs: a coverage cost arising from limitations of the logging policy and a noise cost from imperfect feedback. The temperature $λ$ balances these competing effects, yielding an optimal tradeoff between exploiting high-reward behavior and robustness to noise. Across three public benchmarks and a large-scale industrial dataset, we verify this theoretical prediction: performance follows an inverted-U trend as a function of $λ$, while PPO and DPO often over-optimize unreliable reward models and degrade recommendation quality. Exp-RSFT consistently improves ranking performance without requiring online exploration or preference data.