SynerGraph: An Integrated Graph Convolution Network for Multimodal Recommendation

• URL: http://arxiv.org/abs/2405.19031v1
• Date: Wed, 29 May 2024 12:18:32 GMT
• Title: SynerGraph: An Integrated Graph Convolution Network for Multimodal Recommendation
• Authors: Mert Burabak, Tevfik Aytekin,
• Abstract summary: This article presents a novel approach to multimodal recommendation systems, focusing on integrating and purifying multimodal data. We developed a filter to remove noise from various types of data, making the recommendations more reliable. We studied the impact of top-K sparsification on different datasets, finding optimal values that strike a balance between underfitting and overfitting concerns.
• Score: 1.3812010983144802
• License:
• Abstract: This article presents a novel approach to multimodal recommendation systems, focusing on integrating and purifying multimodal data. Our methodology starts by developing a filter to remove noise from various types of data, making the recommendations more reliable. We studied the impact of top-K sparsification on different datasets, finding optimal values that strike a balance between underfitting and overfitting concerns. The study emphasizes the significant role of textual information compared to visual data in providing a deep understanding of items. We conducted sensitivity analyses to understand how different modalities and the use of purifier circle loss affect the efficiency of the model. The findings indicate that systems that incorporate multiple modalities perform better than those relying on just one modality. Our approach highlights the importance of modality purifiers in filtering out irrelevant data, ensuring that user preferences remain relevant. Models without modality purifiers showed reduced performance, emphasizing the need for effective integration of pre-extracted features. The proposed model, which includes an novel self supervised auxiliary task, shows promise in accurately capturing user preferences. The main goal of the fusion technique is to enhance the modeling of user preferences by combining knowledge with item information, utilizing sophisticated language models. Extensive experiments show that our model produces better results than the existing state-of-the-art multimodal recommendation systems.

Related papers

• Multimodal Information Bottleneck for Deep Reinforcement Learning with Multiple Sensors [10.454194186065195]
  Reinforcement learning has achieved promising results on robotic control tasks but struggles to leverage information effectively. Recent works construct auxiliary losses based on reconstruction or mutual information to extract joint representations from multiple sensory inputs. We argue that compressing information in the learned joint representations about raw multimodal observations is helpful.
  arXiv  Detail & Related papers  (2024-10-23T04:32:37Z)
• DiffMM: Multi-Modal Diffusion Model for Recommendation [19.43775593283657]
  We propose a novel multi-modal graph diffusion model for recommendation called DiffMM. Our framework integrates a modality-aware graph diffusion model with a cross-modal contrastive learning paradigm to improve modality-aware user representation learning.
  arXiv  Detail & Related papers  (2024-06-17T17:35:54Z)
• Corpus Considerations for Annotator Modeling and Scaling [9.263562546969695]
  We show that the commonly used user token model consistently outperforms more complex models. Our findings shed light on the relationship between corpus statistics and annotator modeling performance.
  arXiv  Detail & Related papers  (2024-04-02T22:27:24Z)
• DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
  Current perceptive models heavily depend on resource-intensive datasets. We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability. Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
  arXiv  Detail & Related papers  (2024-03-20T04:58:03Z)
• Debiasing Multimodal Models via Causal Information Minimization [65.23982806840182]
  We study bias arising from confounders in a causal graph for multimodal data. Robust predictive features contain diverse information that helps a model generalize to out-of-distribution data. We use these features as confounder representations and use them via methods motivated by causal theory to remove bias from models.
  arXiv  Detail & Related papers  (2023-11-28T16:46:14Z)
• 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)
• An Empirical Investigation of Commonsense Self-Supervision with Knowledge Graphs [67.23285413610243]
  Self-supervision based on the information extracted from large knowledge graphs has been shown to improve the generalization of language models. We study the effect of knowledge sampling strategies and sizes that can be used to generate synthetic data for adapting language models.
  arXiv  Detail & Related papers  (2022-05-21T19:49:04Z)
• Deep Variational Models for Collaborative Filtering-based Recommender Systems [63.995130144110156]
  Deep learning provides accurate collaborative filtering models to improve recommender system results. Our proposed models apply the variational concept to injectity in the latent space of the deep architecture. Results show the superiority of the proposed approach in scenarios where the variational enrichment exceeds the injected noise effect.
  arXiv  Detail & Related papers  (2021-07-27T08:59:39Z)
• S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization [104.87483578308526]
  We propose the model S3-Rec, which stands for Self-Supervised learning for Sequential Recommendation. For our task, we devise four auxiliary self-supervised objectives to learn the correlations among attribute, item, subsequence, and sequence. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-18T11:44:10Z)

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.