About: An Attention Model of Customer Expectation to Improve Review Helpfulness Prediction

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	An Attention Model of Customer Expectation to Improve Review Helpfulness Prediction
Creator	Li, Xiaopeng Rose, John Farkas, Csilla Qu, Xianshan
Source	PMC
abstract	Many people browse reviews online before making purchasing decisions. It is essential to identify the subset of helpful reviews from the large number of reviews of varying quality. This paper aims to build a model to predict review helpfulness automatically. Our work is inspired by the observation that a customer’s expectation of a review can be greatly affected by review sentiment and the degree to which the customer is aware of pertinent product information. Consequently, a customer may pay more attention to that specific content of a review which contributes more to its helpfulness from their perspective. To model such customer expectations and capture important information from a review text, we propose a novel neural network which leverages review sentiment and product information. Specifically, we encode the sentiment of a review through an attention module, to get sentiment-driven information from review text. We also introduce a product attention layer that fuses information from both the target product and related products, in order to capture the product related information from review text. Our experimental results show an AUC improvement of 5.4% and 1.5% over the previous state of the art model on Amazon and Yelp data sets, respectively.
has issue date	2020-03-17(xsd:dateTime)
bibo:doi	10.1007/978-3-030-45439-5_55
has license	no-cc
sha1sum (hex)	bb3e49473d571697f13e287ec44aafa55184a21b
schema:url	https://doi.org/10.1007/978-3-030-45439-5_55
resource representing a document's title	An Attention Model of Customer Expectation to Improve Review Helpfulness Prediction
has PubMed Central identifier	PMC7148225
schema:publication	Advances in Information Retrieval
resource representing a document's body	covid:bb3e49473d571697f13e287ec44aafa55184a21b#body_text
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