An Empirical Study of Training ID-Agnostic Multi-modal Sequential Recommenders

Alapfogalmak

Multi-modal Sequential Recommendation (MMSR) framework shows potential in enhancing recommendation quality by leveraging multi-modal information without relying on item IDs.

Kivonat

This study explores the effectiveness of a Multi-modal Sequential Recommendation (MMSR) framework in enhancing recommendation quality by leveraging multi-modal information without relying on item IDs. The research systematically summarizes existing multi-modal related SR methods and distills the essence into four core components: visual encoder, text encoder, multimodal fusion module, and sequential architecture. The study delves into constructing MMSR from scratch, benefiting from existing multi-modal pre-training paradigms, and addressing common challenges like cold start and domain transferring. Experimental results across four real-world recommendation scenarios demonstrate the potential of ID-agnostic multi-modal sequential recommendation.
Abstract:

Sequential Recommendation (SR) aims to predict future user-item interactions based on historical interactions.
Multi-modal information is leveraged without using IDs to construct a Multi-Modal Sequential Recommendation (MMSR) framework.
Existing multi-modal related SR methods are systematically summarized into four core components.
The study explores constructing MMSR from scratch, benefiting from multi-modal pre-training paradigms, and addressing common challenges.
Introduction:

SR models aim to recommend the next item of interest based on users' past interactions.
Mainstream SR scenarios rely on user and item IDs, leading to limitations in transferability and cold-start scenarios.
Multi-modal Sequential Recommendation (MMSR) leverages multi-modal information for stronger transferability and addressing cold-start issues.
Experiments:

Various text and vision encoders are explored, with RoBERTa and Swin performing the best.
Different fusion approaches are investigated, with merge-attention outperforming co-attention.
MMSR with different SR architectures shows strong competitiveness, surpassing traditional ID-based SR methods.

Statisztikák

Sequential Recommendation (SR) aims to predict future user-item interactions based on historical interactions.
Multi-modal information is leveraged without using IDs to construct a Multi-Modal Sequential Recommendation (MMSR) framework.
Existing multi-modal related SR methods are systematically summarized into four core components.
The study explores constructing MMSR from scratch, benefiting from existing multi-modal pre-training paradigms, and addressing common challenges like cold start and domain transferring.

Idézetek

"Our framework can be found at: https://github.com/MMSR23/MMSR."

Főbb Kivonatok

An Empirical Study of Training ID-Agnostic Multi-modal Sequential Recommenders

by Youhua Li,Ha... : arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17372.pdf

Mélyebb kérdések

How can advancements in multi-modal pre-training paradigms enhance the field of recommendation systems?

Advancements in multi-modal pre-training paradigms can greatly enhance recommendation systems by improving the understanding and representation of multi-modal data. By leveraging pre-trained models from the Vision-and-Language Pre-training (VLP) community, recommendation systems can benefit from more robust and comprehensive representations of items, combining both text and visual information. These pre-trained models can capture complex relationships between different modalities, leading to more accurate and contextually rich recommendations. Additionally, the deep fusion modules in VLP models can help in integrating and processing multi-modal information effectively, enhancing the overall performance of recommendation systems.

What are the potential drawbacks of relying solely on item IDs in sequential recommendation models?

Relying solely on item IDs in sequential recommendation models can have several drawbacks. One major limitation is the lack of transferability across different domains and platforms. Item IDs are specific to each dataset and cannot be easily shared or transferred to new environments, making it challenging to apply the model to diverse recommendation scenarios. Additionally, ID-based models may struggle with cold-start problems, where recommendations for new or less frequently seen items are challenging due to the lack of historical interactions. Furthermore, ID-based models may not capture the rich semantic information present in multi-modal data, limiting the model's ability to understand and recommend items based on their textual and visual features.

How can the findings of this study be applied to other domains beyond recommendation systems?

The findings of this study can be applied to other domains beyond recommendation systems by leveraging the insights and methodologies developed for multi-modal sequential recommendation. The concept of integrating text and visual information to enhance recommendation performance can be extended to various fields such as content understanding, image recognition, and natural language processing. By adopting a multi-modal approach and utilizing pre-trained models from the VLP community, other domains can benefit from improved data representation, transfer learning capabilities, and enhanced performance in handling cold-start scenarios. The modular and flexible framework proposed in the study can be adapted and implemented in diverse domains to leverage the power of multi-modal learning for better outcomes.