Category-aware Graph Neural Networks for Improving E-commerce Review Helpfulness Prediction

Xiaoru, Qu; Li, Zhao; Wang, Jialin; Zhang, Zhipeng; Zou, Pengcheng; Jiang, Junxiao; Huang, Jiaming; Xiao, Rong; Zhang, Ji; Gao, Jun

doi:10.1145/3340531.3412691

Cited by 16 publications

(6 citation statements)

References 15 publications

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“…To this end, a deep neural architecture was proposed to capture the intrinsic relationship between the meta-data of a product and its numerous reviews. Qu et al (2020) proposed to leverage the reviews, the users, and items together for helpfulness prediction of reviews and devised a categoryaware graph neural networks with one shared and many item-specific graph convolutions to learn the common features and each item's specific criterion for helpfulness prediction.…”

Section: Related Workmentioning

confidence: 99%

Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews

Liu¹,

Yang²

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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As more and more product reviews are posted in both text and images, Multimodal Review Analysis (MRA) becomes an attractive research topic. Among the existing review analysis tasks, helpfulness prediction on review text has become predominant due to its importance for e-commerce platforms and online shops, i.e. helping customers quickly acquire useful product information. This paper proposes a new task Multimodal Review Helpfulness Prediction (MRHP) aiming to analyze the review helpfulness from text and visual modalities. Meanwhile, a novel Multi-perspective Coherent Reasoning method (MCR) is proposed to solve the MRHP task, which conducts joint reasoning over texts and images from both the product and the review, and aggregates the signals to predict the review helpfulness. Concretely, we first propose a productreview coherent reasoning module to measure the intra-and inter-modal coherence between the target product and the review. In addition, we also devise an intra-review coherent reasoning module to identify the coherence between the text content and images of the review, which is a piece of strong evidence for review helpfulness prediction. To evaluate the effectiveness of MCR, we present two newly collected multimodal review datasets as benchmark evaluation resources for the MRHP task. Experimental results show that our MCR method can lead to a performance increase of up to 8.5% as compared to the best performing text-only model. The source code and datasets can be obtained from https:// github.com/jhliu17/MCR.

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Section: Related Workmentioning

confidence: 99%

Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews

Liu¹,

Yang²

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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“…Bitcoin-Alpha 3 and Bitcoin-OTC 4 [42], [43] are two who-trusts-whom networks of bitcoin users trading on the platforms from www.btc-alpha.com and www.bitcoinotc.com respectively. In these two datasets, the nodes are the users from the platform, and an edge appears when one user rates another on the platform.…”

Section: Datasetsmentioning

confidence: 99%

“…S INCE graphs are used to represent the complex systems in various domains as diverse as social network [1], human knowledge network [2], e-commerce [3] and cybersecurity [4], the analysis and mining for graph data have attracted a surge of research attention in recent years. However, the bulk of the existing researches focus on static graphs, yet the real-world graph data often evolves over time [5].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in Dynamic Graphs via Transformer

Liu,

Pan,

Wang

et al. 2021

Preprint

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Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide applications in social networks, e-commerce, and cybersecurity. The recent deep learning-based approaches have shown promising results over shallow methods. However, they fail to address two core challenges of anomaly detection in dynamic graphs: the lack of informative encoding for unattributed nodes and the difficulty of learning discriminate knowledge from coupled spatial-temporal dynamic graphs. To overcome these challenges, in this paper, we present a novel Transformer-based Anomaly Detection framework for DYnamic graph (TADDY). Our framework constructs a comprehensive node encoding strategy to better represent each node's structural and temporal roles in an evolving graphs stream. Meanwhile, TADDY captures informative representation from dynamic graphs with coupled spatial-temporal patterns via a dynamic graph transformer model. The extensive experimental results demonstrate that our proposed TADDY framework outperforms the state-of-the-art methods by a large margin on four real-world datasets.

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“…The fewer votes on latest products lead to bias errors and are not credible. Therefore, it is necessary to automatically evaluate the review helpfulness [4].…”

Section: Introductionmentioning

confidence: 99%

Deep semi-supervised learning with weight map for review helpfulness prediction

Yin

Peng

et al. 2021

COMSIS J

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Helpful online product reviews, which includemassive information, have large impacts on customers? purchasing decisions. In most of e-commerce plat forms, the helpfulness of reviews are decided by the votes from other customers. Making full use of these reviews with votes has enormous commercial value, especially in product recommendation. It drives researchers to study the technologies about how to evaluate the review helpfulness automatically. Although Deep Neural Network(DNN), learning from the historical reviews and labels, computed by the votes, has demonstrated effective results, it still has suffered insufficient labeled reviews problem. When the helpfulness of a large number of reviews is unknown for lack of votes, or some useful latest reviews with less votes are submerged by the past reviews, the accuracy of current DNN model decreases quickly. Therefore, we propose an end-to-end deep semi-supervised learning model with weight map, which makes full use of the unlabeled reviews. The training process in this model is divided into three stages:obtaining base classifier by less labeled reviews, iteratively applying weight map strategy on large unlabeled reviews to obtain pseudo-labeled reviews, training on above combined reviews to obtain the re-training classifier. Based on this novel model, we develop an algorithm and conduct a series of experiments, on Amazon Review Dataset, from the aspects of the baseline neural network selection and the strategies comparisons, including two labeling and three weighting strategies. The experimental results demonstrate the effectiveness of our method on utilizing the unlabeled data. And our findings show that the model adopted batch labeling strategy and non-linear weight mapping method has achieved the best performance.

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Category-aware Graph Neural Networks for Improving E-commerce Review Helpfulness Prediction

Cited by 16 publications

References 15 publications

Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews

Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews

Anomaly Detection in Dynamic Graphs via Transformer

Deep semi-supervised learning with weight map for review helpfulness prediction

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