Ducho meets Elliot: Large-scale Benchmarks for Multimodal Recommendation (2409.15857v1)

Abstract: In specific domains like fashion, music, and movie recommendation, the multi-faceted features characterizing products and services may influence each customer on online selling platforms differently, paving the way to novel multimodal recommendation models that can learn from such multimodal content. According to the literature, the common multimodal recommendation pipeline involves (i) extracting multimodal features, (ii) refining their high-level representations to suit the recommendation task, (iii) optionally fusing all multimodal features, and (iv) predicting the user-item score. While great effort has been put into designing optimal solutions for (ii-iv), to the best of our knowledge, very little attention has been devoted to exploring procedures for (i). In this respect, the existing literature outlines the large availability of multimodal datasets and the ever-growing number of large models accounting for multimodal-aware tasks, but (at the same time) an unjustified adoption of limited standardized solutions. This motivates us to explore more extensive techniques for the (i) stage of the pipeline. To this end, this paper settles as the first attempt to offer a large-scale benchmarking for multimodal recommender systems, with a specific focus on multimodal extractors. Specifically, we take advantage of two popular and recent frameworks for multimodal feature extraction and reproducibility in recommendation, Ducho and Elliot, to offer a unified and ready-to-use experimental environment able to run extensive benchmarking analyses leveraging novel multimodal feature extractors. Results, largely validated under different hyper-parameter settings for the chosen extractors, provide important insights on how to train and tune the next generation of multimodal recommendation algorithms.

• The paper introduces an integrated benchmarking framework combining Ducho’s feature extraction and Elliot’s evaluation for multimodal recommendations.
• It systematically compares six feature extractors and twelve recommender models across five diverse Amazon datasets to determine performance gains.
• Findings reveal that multimodal-by-design extractors like CLIP and AltCLIP enhance recommendation performance while optimizing computational efficiency.

A Comprehensive Benchmarking Study for Multimodal Recommendation: Integrating Ducho with Elliot

The paper "Ducho meets Elliot: Large-scale Benchmarks for Multimodal Recommendation" presents a detailed benchmarking paper on multimodal recommendation systems by integrating two existing frameworks, Ducho and Elliot. The primary focus is on evaluating different multimodal feature extraction techniques and their impact on recommendation performance. This paper addresses several critical gaps in the literature by providing a standardized and unified experimental environment for multimodal recommendation benchmarking.

Research Context and Motivation

The literature on multimodal recommendation has significantly advanced with the advent of deep learning and large multimodal models. However, a notable gap persists in the careful examination of multimodal feature extraction techniques—specifically, the Which? stage of the multimodal recommendation pipeline, involving feature extraction from multimodal content. The authors identify this lack as a critical bottleneck and aim to provide a comprehensive benchmark, leveraging Ducho for feature extraction and Elliot for recommendation system evaluation.

Experimental Setup and Datasets

The authors conducted an extensive experiment involving five popular datasets from the Amazon catalog across different product categories (Office Products, Digital Music, Baby, Toys {content} Games, and Beauty). The datasets feature user-item interactions along with item metadata such as images and descriptions.

Multimodal Feature Extractors

Six multimodal feature extractors were considered, including both classical and multimodal-by-design models:

1. ResNet50 (RNet50) - For visual features
2. MMFashion (MMF) - A domain-specific visual feature extractor
3. Sentence-BERT (SBert) - For textual feature extraction
4. CLIP - A multimodal-by-design model combining visual and textual features
5. Align - Another multimodal model trained on noisy image-caption pairs
6. AltCLIP - An enhanced version of CLIP with multilingual capabilities

Recommendation Models

Twelve state-of-the-art recommender systems were evaluated, including classic models (e.g., ItemKNN, BPRMF) and multimodal recommendation models (e.g., VBPR, NGCF-M, GRCN, LATTICE, BM3, FREEDOM). These systems were trained and evaluated using the Elliot framework.

Results and Findings

RQ1: Efficacy of Ducho + Elliot Pipeline

The integration of Ducho with Elliot was validated through extensive benchmarking, demonstrating that this combined environment can effectively benchmark state-of-the-art multimodal recommender systems. Multimodal models consistently outperformed classical models across all metrics and datasets.

RQ2: Impact of Different Feature Extractors

The paper found that the use of recent multimodal-by-design extractors (CLIP, Align, AltCLIP) significantly improves recommendation performance over traditional extractors like RNet50 and SBert. For example, CLIP and AltCLIP showed superior performance in several datasets, highlighting their potential in capturing complex multimodal relationships.

RQ3: Influence of Hyper-Parameters on Performance

The authors investigated the computational complexity associated with different batch sizes. It was demonstrated that increasing the batch size could drastically reduce extraction time without a significant loss in recommendation performance. This finding suggests that an optimal trade-off between computational efficiency and recommendation quality is achievable.

Implications and Future Directions

The implications of this research are multifaceted. Practically, the paper provides a robust framework for evaluating multimodal recommendation systems using a wide range of feature extractors. Theoretically, it underscores the importance of considering advanced multimodal feature extractors to enhance recommendation quality. The authors also suggest extending this work by integrating more recent multimodal models and exploring additional performance metrics such as novelty, diversity, bias, and fairness.

Summary

This comprehensive paper provides critical insights into the performance of state-of-the-art multimodal recommender systems, highlighting the potential of multimodal-by-design feature extractors. By integrating Ducho and Elliot, the authors offer a standardized and reproducible environment for extensive benchmarking in multimodal recommendation, setting a solid foundation for future research in this domain.