Ducho meets Elliot: Large-scale Benchmarks for Multimodal Recommendation

• URL: http://arxiv.org/abs/2409.15857v1
• Date: Tue, 24 Sep 2024 08:29:10 GMT
• Title: Ducho meets Elliot: Large-scale Benchmarks for Multimodal Recommendation
• Authors: Matteo Attimonelli, Danilo Danese, Angela Di Fazio, Daniele Malitesta, Claudio Pomo, Tommaso Di Noia,
• Abstract summary: Multi-faceted features characterizing products and services may influence each customer on online selling platforms differently. The common multimodal recommendation pipeline involves (i) extracting multimodal features, (ii) refining their high-level representations to suit the recommendation task, and (iv) predicting the user-item score. This paper settles as the first attempt to offer a large-scale benchmarking for multimodal recommender systems, with a specific focus on multimodal extractors.
• Score: 9.506245109666907
• License:
• 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.

Related papers

• Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization [65.64108848398696]
  We introduce a preference optimization process to enhance the multimodal reasoning capabilities of MLLMs. We develop a simple yet effective method, termed Mixed Preference Optimization (MPO), which boosts multimodal CoT performance. Our model, InternVL2-8B-MPO, achieves an accuracy of 67.0 on MathVista, outperforming InternVL2-8B by 8.7 points and achieving performance comparable to the 10x larger InternVL2-76B.
  arXiv  Detail & Related papers  (2024-11-15T18:59:27Z)
• M$^2$PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning [90.75075886543404]
  Multimodal Large Language Models (MLLMs) demonstrate remarkable performance across a wide range of domains. In this work, we introduce a novel Multimodal Prompt Tuning (M$2$PT) approach for efficient instruction tuning of MLLMs.
  arXiv  Detail & Related papers  (2024-09-24T01:40:24Z)
• Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation? [15.428850539237182]
  We show that the lack of (some) modalities is, in fact, a widely-diffused phenomenon in multimodal recommendation. We propose a pipeline that imputes missing multimodal features in recommendation by leveraging traditional imputation strategies in machine learning.
  arXiv  Detail & Related papers  (2024-08-21T16:39:47Z)
• A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendation [9.720586396359906]
  We argue that existing multi-modal recommender systems typically use isolated processes for both feature extraction and modality modelling. We propose a novel model, called Unified Multi-modal Graph Transformer (UGT), which leverages a multi-way transformer to extract aligned multi-modal features. We show that the UGT model can achieve significant effectiveness gains, especially when jointly optimised with the commonly-used multi-modal recommendation losses.
  arXiv  Detail & Related papers  (2024-07-29T11:04:31Z)
• Towards Bridging the Cross-modal Semantic Gap for Multi-modal Recommendation [12.306686291299146]
  Multi-modal recommendation greatly enhances the performance of recommender systems. Most existing multi-modal recommendation models exploit multimedia information propagation processes to enrich item representations. We propose a novel framework to bridge the semantic gap between modalities and extract fine-grained multi-view semantic information.
  arXiv  Detail & Related papers  (2024-07-07T15:56:03Z)
• U3M: Unbiased Multiscale Modal Fusion Model for Multimodal Semantic Segmentation [63.31007867379312]
  We introduce U3M: An Unbiased Multiscale Modal Fusion Model for Multimodal Semantics. We employ feature fusion at multiple scales to ensure the effective extraction and integration of both global and local features. Experimental results demonstrate that our approach achieves superior performance across multiple datasets.
  arXiv  Detail & Related papers  (2024-05-24T08:58:48Z)
• Multimodal Pretraining and Generation for Recommendation: A Tutorial [54.07497722719509]
  The tutorial comprises three parts: multimodal pretraining, multimodal generation, and industrial applications. It aims to facilitate a swift understanding of multimodal recommendation and foster meaningful discussions on the future development of this evolving landscape.
  arXiv  Detail & Related papers  (2024-05-11T06:15:22Z)
• Generative Multimodal Models are In-Context Learners [60.50927925426832]
  We introduce Emu2, a generative multimodal model with 37 billion parameters, trained on large-scale multimodal sequences. Emu2 exhibits strong multimodal in-context learning abilities, even emerging to solve tasks that require on-the-fly reasoning.
  arXiv  Detail & Related papers  (2023-12-20T18:59:58Z)
• Exploiting Modality-Specific Features For Multi-Modal Manipulation Detection And Grounding [54.49214267905562]
  We construct a transformer-based framework for multi-modal manipulation detection and grounding tasks. Our framework simultaneously explores modality-specific features while preserving the capability for multi-modal alignment. We propose an implicit manipulation query (IMQ) that adaptively aggregates global contextual cues within each modality.
  arXiv  Detail & Related papers  (2023-09-22T06:55:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.