Customizing Language Models with Instance-wise LoRA for Sequential Recommendation (2408.10159v3)

Abstract: Sequential recommendation systems predict the next interaction item based on users' past interactions, aligning recommendations with individual preferences. Leveraging the strengths of LLMs in knowledge comprehension and reasoning, recent approaches are eager to apply LLMs to sequential recommendation. A common paradigm is converting user behavior sequences into instruction data, and fine-tuning the LLM with parameter-efficient fine-tuning (PEFT) methods like Low-Rank Adaption (LoRA). However, the uniform application of LoRA across diverse user behaviors is insufficient to capture individual variability, resulting in negative transfer between disparate sequences. To address these challenges, we propose Instance-wise LoRA (iLoRA). We innovatively treat the sequential recommendation task as a form of multi-task learning, integrating LoRA with the Mixture of Experts (MoE) framework. This approach encourages different experts to capture various aspects of user behavior. Additionally, we introduce a sequence representation guided gate function that generates customized expert participation weights for each user sequence, which allows dynamic parameter adjustment for instance-wise recommendations. In sequential recommendation, iLoRA achieves an average relative improvement of 11.4\% over basic LoRA in the hit ratio metric, with less than a 1\% relative increase in trainable parameters. Extensive experiments on three benchmark datasets demonstrate the effectiveness of iLoRA, highlighting its superior performance compared to existing methods in mitigating negative transfer and improving recommendation accuracy. Our data and code are available at https://github.com/AkaliKong/iLoRA.

Customizing LLMs with Instance-wise LoRA for Sequential Recommendation

The paper "Customizing LLMs with Instance-wise LoRA for Sequential Recommendation" presents a novel approach to address challenges in sequential recommendation systems by leveraging LLMs. It innovates upon existing methodologies by introducing the Instance-wise Low-Rank Adaptation (iLoRA) framework, which selectively fine-tunes LLMs to optimize recommendation tasks.

Core Contributions

Sequential recommendation aims to predict a user's next item of interest based on their historical interaction data. Traditional approaches often fail to capture individual variability due to a generalized application of existing models. This paper acknowledges the limitations inherent in using a uniform Low-Rank Adaptation (LoRA) across varied user behaviors, leading to inefficiencies and negative transfer.

To address this, the paper introduces Instance-wise LoRA (iLoRA), which treats sequential recommendation as a multi-task learning problem, guided by the Mixture of Experts (MoE) framework. The significant contributions of iLoRA include:

1. Integration with MoE Framework: By combining LoRA with MoE, the proposed method allows different "experts" to specialize in capturing various user behavior aspects. Such specialization aligns each task with a particular behavioral pattern, improving the recommendation's fidelity and reducing negative transfer across sequences.
2. Dynamic Parameter Customization: The paper highlights the introduction of a sequence representation-guided gating mechanism that dynamically adjusts expert contributions. This customization ensures that the parameters are optimized for individual sequences, leading to an 11.4% relative improvement in hit ratio over traditional methods with a LoRA module, while keeping trainable parameters increment under 1%.
3. Comprehensive Evaluation: Extensive experiments conducted on benchmark datasets such as LastFM, MovieLens, and Steam demonstrate iLoRA's superiority over both classical and LLM-based recommendation systems. The method's ability to mitigate adverse effects of uniform LoRA, while enhancing accuracy and relevance, sets a new standard in personalized recommendation tasks.

Implications and Future Directions

This research has substantial implications for the field of AI-driven personalization, exemplified by its enhanced performance in sequential recommendation. The emphasis on individual variability and dynamic adaptation offers a pathway toward more sophisticated recommendation systems that better cater to the nuances of personal user behavior.

Theoretically, iLoRA contributes to the discourse on parameter-efficient fine-tuning techniques, aligning LLMs with the diverse dimensions of user interaction data. It underscores the potential of multi-task frameworks in unlocking deeper model insights and utility.

Practically, adopting iLoRA could result in more effective recommendations in sectors such as e-commerce, entertainment, and content streaming where understanding user preference trajectories is crucial. It paves the way for more efficient computational practices by minimizing resource demands while maximizing customization.

Future research could explore the scalability of iLoRA in real-world, high-demand environments, and investigate the integration of more complex, multi-modal data into this framework. Additionally, a deeper exploration of iLoRA's applicability to other domains, such as healthcare and finance, where sequential data is pivotal, could further expand its impact.

In summary, the paper offers a significant advancement in sequential recommendation by leveraging LLM adaptability through the novel application of instance-customized LoRA, demonstrating both theoretical innovation and practical applicability.