2,530 research outputs found
Exploring Latent Semantic Factors to Find Useful Product Reviews
Online reviews provided by consumers are a valuable asset for e-Commerce
platforms, influencing potential consumers in making purchasing decisions.
However, these reviews are of varying quality, with the useful ones buried deep
within a heap of non-informative reviews. In this work, we attempt to
automatically identify review quality in terms of its helpfulness to the end
consumers. In contrast to previous works in this domain exploiting a variety of
syntactic and community-level features, we delve deep into the semantics of
reviews as to what makes them useful, providing interpretable explanation for
the same. We identify a set of consistency and semantic factors, all from the
text, ratings, and timestamps of user-generated reviews, making our approach
generalizable across all communities and domains. We explore review semantics
in terms of several latent factors like the expertise of its author, his
judgment about the fine-grained facets of the underlying product, and his
writing style. These are cast into a Hidden Markov Model -- Latent Dirichlet
Allocation (HMM-LDA) based model to jointly infer: (i) reviewer expertise, (ii)
item facets, and (iii) review helpfulness. Large-scale experiments on five
real-world datasets from Amazon show significant improvement over
state-of-the-art baselines in predicting and ranking useful reviews
HETEROGENEOUS GRAPH-BASED USER-SPECIFIC REVIEW HELPFULNESS PREDICTION
With the popularity of e-commerce and review websites, it is becoming increasingly important to identify the helpfulness of reviews. However, existing works on predicting reviewsâ helpfulness have three major issues: (i) the correlation between helpfulness and features from review text is not clear yet, although many standard features are proposed, (ii) the relations between users, reviews and products have not been considered, (iii) the eďŹectiveness of the existing approaches have not been systematically compared. To address these challenges, we ďŹrst analyze the correlation between standard features and review helpfulness that are widely used in other work. Based on this analysis, we propose an end-to-end neural network architecture, the Global-Local Heterogeneous Graph Neural Networks (GL-HGNN). It consists of the graph construction and learning nodes representations both globally and locally. The graph is composed of three types of nodes including users, reviews and products, as well as four link types to build connections among these nodes. To better learn the feature representations, we employ a global graph neural network (GNN) branch and a local GNN branch on the whole graph and associated subgraphs to capture graph structure and information propagation. Finally, we provide an empirical comparison with traditional machine learning models training on hand-crafted features as well as four state-of-the-art deep learning models on eight Amazon product categories
Exploring determinants of attraction and helpfulness of online product review:a consumer behaviour perspective
To assist filtering and sorting massive review messages, this paper attempts to examine the determinants of review attraction and helpfulness. Our analysis divides consumersâ reading process into ânotice stageâ and âcomprehend stageâ and considers the impact of âexplicit informationâ and âimplicit informationâ of review attraction and review helpfulness. 633 online product reviews were collected from Amazon China. A mixed-method approach is employed to test the conceptual model proposed for examining the influencing factors of review attraction and helpfulness. The empirical results show that reviews with negative extremity, more words, and higher reviewer rank easily gain more attraction and reviews with negative extremity, higher reviewer rank, mixed subjective property, and mixed sentiment seem to be more helpful. The research findings provide some important insights, which will help online businesses to encourage consumers to write good quality reviews and take more active actions to maximise the value of online reviews
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning
CHI â22, April 29-May 5, 2022, New Orleans, LA, USA Š 2022 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-9157-3/22/04. https://doi.org/10.1145/3491102.3517522Model explanations such as saliency maps can improve user trust in AI by highlighting important features for a prediction. However, these become distorted and misleading when explaining predictions of images that are subject to systematic error (bias). Furthermore, the distortions persist despite model fine-tuning on images biased by different factors (blur, color temperature, day/night). We present Debiased-CAM to recover explanation faithfulness across various bias types and levels by training a multi-input, multi-task model with auxiliary tasks for explanation and bias level predictions. In simulation studies, the approach not only enhanced prediction accuracy, but also generated highly faithful explanations about these predictions as if the images were unbiased. In user studies, debiased explanations improved user task performance, perceived truthfulness and perceived helpfulness. Debiased training can provide a versatile platform for robust performance and explanation faithfulness for a wide range of applications with data biases.Peer ReviewedPostprint (published version
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