LLMSeR: Enhancing Sequential Recommendation via LLM-based Data Augmentation
LLMSeR enhances sequential recommendation by generating pseudo-prior items using LLMs.
Key Findings
Methodology
LLMSeR framework generates pseudo-prior items using LLMs, integrating semantic and collaborative information to enhance user interaction data. Core components include Semantic Interaction Augmentor and Adaptive Reliability Validation to ensure data reliability.
Key Results
- On the Fashion dataset, LLMSeR improved GRU4Rec's H@10 metric to 0.4346, significantly outperforming baseline methods.
- On the Book dataset, LLMSeR achieved an H@10 improvement of 0.4045 on Bert4Rec.
- Ablation studies show performance drops when any component is removed.
Significance
LLMSeR holds significant academic and industrial value, particularly in addressing the long-tail user problem. By generating pseudo-prior items, it improves user experience and enhances overall recommendation system performance.
Technical Contribution
LLMSeR introduces a novel data augmentation method that combines semantic and collaborative information, overcoming issues of missing collaborative signals and hallucination phenomena in existing methods.
Novelty
LLMSeR is the first to use LLMs for data augmentation in sequential recommendation, innovatively introducing an Adaptive Reliability Validation module to ensure high reliability of generated data.
Limitations
- Generating pseudo-prior items for extremely long-tail users may still introduce noise.
- Dependence on LLMs may lead to high computational costs.
Future Work
Future work could explore more efficient pseudo-prior item generation methods and applications in more domains.
AI Executive Summary
Sequential Recommender Systems (SRS) play a crucial role in fields like e-commerce, but their performance is limited by the long-tail user problem. Existing methods mainly rely on collaborative information, which is susceptible to noise. LLMSeR enhances recommendation performance by generating pseudo-prior items using large language models, integrating semantic and collaborative information.
The core innovations of LLMSeR lie in the Semantic Interaction Augmentor and Adaptive Reliability Validation modules. The former generates pseudo-prior items by combining semantic and collaborative information, while the latter evaluates the reliability of these items, ensuring effective data augmentation. Experiments demonstrate that LLMSeR outperforms existing methods across multiple datasets.
Despite significant performance improvements, LLMSeR has limitations, such as computational costs due to dependence on large language models. Future research could further optimize pseudo-prior item generation methods and explore more application scenarios.
Deep Analysis
Background
Sequential Recommender Systems use historical user interaction data to predict future behavior and are widely used in fields like e-commerce. However, the long-tail user problem limits their performance, especially for users with fewer interaction records.
Core Problem
The long-tail user problem is a core challenge for sequential recommender systems. Most users have few interaction records, leading to poor recommendation performance. Existing methods mainly rely on collaborative information, which is prone to noise.
Innovation
LLMSeR generates pseudo-prior items using large language models, combining semantic and collaborative information to innovatively address issues of missing collaborative signals and hallucination phenomena.
Methodology
- �� Use large language models to generate pseudo-prior items, integrating semantic and collaborative information.
- �� Semantic Interaction Augmentor generates pseudo-prior items.
- �� Adaptive Reliability Validation module evaluates the reliability of pseudo-prior items.
Experiments
Experiments were conducted on Fashion, Book, and Yelp datasets, using GRU4Rec, Bert4Rec, and SASRec as baseline models to evaluate LLMSeR's performance improvements.
Results
LLMSeR outperformed baseline methods across all datasets and models, with significant improvements for long-tail users.
Applications
LLMSeR can be applied to e-commerce platforms to improve the accuracy of personalized recommendations and enhance user experience.
Limitations & Outlook
Dependence on large language models may lead to high computational costs. Future work could explore more efficient pseudo-prior item generation methods.
Plain Language Accessible to non-experts
Imagine you're in a library trying to find books you might like. Traditional methods recommend based on books you've borrowed, but if you've only borrowed a few, the recommendations aren't great. LLMSeR is like a smart librarian who not only looks at the books you've borrowed but also considers the content and other readers' reviews to guess books you might like, giving you more choices.
ELI14 Explained like you're 14
Imagine you're playing a game, and the game recommends new games based on your past plays. If you've only played a few games, the recommendations aren't accurate. LLMSeR is like a super-smart assistant that looks at the games you've played and other players' choices to guess games you might like, giving you more fun recommendations!
Glossary
Sequential Recommender Systems
Systems that predict future user behavior based on historical interaction data.
Used to predict the next potential interaction of a user.
Long-tail User Problem
The issue of poor recommendation performance for users with fewer interaction records.
Leads to unsatisfactory user experience in SRS.
Large Language Models
Large-scale neural network models capable of understanding semantic relationships and generating text.
Used to generate pseudo-prior items for data augmentation.
Semantic Interaction Augmentor
A module that generates pseudo-prior items by combining semantic and collaborative information.
Used to generate more reliable user interaction data.
Adaptive Reliability Validation
A module that evaluates the reliability of pseudo-prior items, ensuring effective data augmentation.
Used to mitigate the impact of hallucination phenomena.
Open Questions Unanswered questions from this research
- 1 How to generate high-quality pseudo-prior items without relying on large language models?
- 2 How to further reduce computational costs in the pseudo-prior item generation process?
Applications
Immediate Applications
E-commerce Recommendation
Improve the accuracy of personalized recommendations using LLMSeR to enhance user shopping experience.
Long-term Vision
Cross-domain Applications
Apply LLMSeR to other fields like social media recommendations to increase user engagement.
Abstract
Sequential Recommender Systems (SRS) have become a cornerstone of online platforms, leveraging users' historical interaction data to forecast their next potential engagement. Despite their widespread adoption, SRS often grapple with the long-tail user dilemma, resulting in less effective recommendations for individuals with limited interaction records. The advent of Large Language Models (LLMs), with their profound capability to discern semantic relationships among items, has opened new avenues for enhancing SRS through data augmentation. Nonetheless, current methodologies encounter obstacles, including the absence of collaborative signals and the prevalence of hallucination phenomena. In this work, we present LLMSeR, an innovative framework that utilizes Large Language Models (LLMs) to generate pseudo-prior items, thereby improving the efficacy of Sequential Recommender Systems (SRS). To alleviate the challenge of insufficient collaborative signals, we introduce the Semantic Interaction Augmentor (SIA), a method that integrates both semantic and collaborative information to comprehensively augment user interaction data. Moreover, to weaken the adverse effects of hallucination in SRS, we develop the Adaptive Reliability Validation (ARV), a validation technique designed to assess the reliability of the generated pseudo items. Complementing these advancements, we also devise a Dual-Channel Training strategy, ensuring seamless integration of data augmentation into the SRS training process.Extensive experiments conducted with three widely-used SRS models demonstrate the generalizability and efficacy of LLMSeR.