A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendation

• URL: http://arxiv.org/abs/2407.19886v1
• Date: Mon, 29 Jul 2024 11:04:31 GMT
• Title: A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendation
• Authors: Zixuan Yi, Iadh Ounis,
• Abstract summary: 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.
• Score: 9.720586396359906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rapid development of online multimedia services, especially in e-commerce platforms, there is a pressing need for personalised recommendation systems that can effectively encode the diverse multi-modal content associated with each item. However, we argue that existing multi-modal recommender systems typically use isolated processes for both feature extraction and modality modelling. Such isolated processes can harm the recommendation performance. Firstly, an isolated extraction process underestimates the importance of effective feature extraction in multi-modal recommendations, potentially incorporating non-relevant information, which is harmful to item representations. Second, an isolated modality modelling process produces disjointed embeddings for item modalities due to the individual processing of each modality, which leads to a suboptimal fusion of user/item representations for effective user preferences prediction. We hypothesise that the use of a unified model for addressing both aforementioned isolated processes will enable the consistent extraction and cohesive fusion of joint multi-modal features, thereby enhancing the effectiveness of multi-modal recommender systems. In this paper, we propose a novel model, called Unified Multi-modal Graph Transformer (UGT), which firstly leverages a multi-way transformer to extract aligned multi-modal features from raw data for top-k recommendation. Subsequently, we build a unified graph neural network in our UGT model to jointly fuse the user/item representations with their corresponding multi-modal features. Using the graph transformer architecture of our UGT model, we show that the UGT model can achieve significant effectiveness gains, especially when jointly optimised with the commonly-used multi-modal recommendation losses.

Related papers

• QARM: Quantitative Alignment Multi-Modal Recommendation at Kuaishou [23.818456863262494]
  We introduce a quantitative multi-modal framework to customize the specialized and trainable multi-modal information for different downstream models. Inspired by the two difficulties challenges in downstream tasks usage, we introduce a quantitative multi-modal framework to customize the specialized and trainable multi-modal information for different downstream models.
  arXiv  Detail & Related papers  (2024-11-18T17:08:35Z)
• 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)
• Ducho meets Elliot: Large-scale Benchmarks for Multimodal Recommendation [9.506245109666907]
  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.
  arXiv  Detail & Related papers  (2024-09-24T08:29:10Z)
• 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)
• MMGRec: Multimodal Generative Recommendation with Transformer Model [81.61896141495144]
  MMGRec aims to introduce a generative paradigm into multimodal recommendation. We first devise a hierarchical quantization method Graph CF-RQVAE to assign Rec-ID for each item from its multimodal information. We then train a Transformer-based recommender to generate the Rec-IDs of user-preferred items based on historical interaction sequences.
  arXiv  Detail & Related papers  (2024-04-25T12:11:27Z)
• Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding [62.70450216120704]
  Unsupervised pre-training has shown great success in skeleton-based action understanding. We propose a Unified Multimodal Unsupervised Representation Learning framework, called UmURL. UmURL exploits an efficient early-fusion strategy to jointly encode the multi-modal features in a single-stream manner.
  arXiv  Detail & Related papers  (2023-11-06T13:56:57Z)
• 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)
• MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation [61.45986275328629]
  We propose MISSRec, a multi-modal pre-training and transfer learning framework for sequential recommendation. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal user interests. On the candidate item side, we adopt a dynamic fusion module to produce user-adaptive item representation.
  arXiv  Detail & Related papers  (2023-08-22T04:06:56Z)
• Multimodal E-Commerce Product Classification Using Hierarchical Fusion [0.0]
  The proposed method significantly outperformed the unimodal models' performance and the reported performance of similar models on our specific task. We did experiments with multiple fusing techniques and found, that the best performing technique to combine the individual embedding of the unimodal network is based on combining concatenation and averaging the feature vectors.
  arXiv  Detail & Related papers  (2022-07-07T14:04:42Z)

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.