ATFLRec: A Multimodal Recommender System with Audio-Text Fusion and Low-Rank Adaptation via Instruction-Tuned Large Language Model

TL;DR

This paper introduces ATFLRec, a multimodal recommender system that fuses audio and text data using instruction-tuned large language models with low-rank adaptation, improving recommendation accuracy and efficiency.

Contribution

The study proposes a novel multimodal recommendation framework integrating audio and text into LLMs with LoRA, addressing cold-start and computational challenges.

Findings

ATFLRec outperforms baseline models in AUC scores.

Separate LoRA modules for audio and text improve performance.

Modality fusion techniques and pooling methods significantly affect results.

Abstract

Recommender Systems (RS) play a pivotal role in boosting user satisfaction by providing personalized product suggestions in domains such as e-commerce and entertainment. This study examines the integration of multimodal data text and audio into large language models (LLMs) with the aim of enhancing recommendation performance. Traditional text and audio recommenders encounter limitations such as the cold-start problem, and recent advancements in LLMs, while promising, are computationally expensive. To address these issues, Low-Rank Adaptation (LoRA) is introduced, which enhances efficiency without compromising performance. The ATFLRec framework is proposed to integrate audio and text modalities into a multimodal recommendation system, utilizing various LoRA configurations and modality fusion techniques. Results indicate that ATFLRec outperforms baseline models, including traditional and graph neural network-based approaches, achieving higher AUC scores. Furthermore, separate fine-tuning of audio and text data with distinct LoRA modules yields optimal performance, with different pooling methods and Mel filter bank numbers significantly impacting performance. This research offers valuable insights into optimizing multimodal recommender systems and advancing the integration of diverse data modalities in LLMs.