Product-Aware Helpfulness Prediction of Online Reviews

Fan, Miao; Feng, Chao; Guo, L. Jay; Sun, Mingming; Li, Ping

doi:10.1145/3308558.3313523

Cited by 37 publications

(20 citation statements)

References 16 publications

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“…Networks have recently been proposed for helpfulness prediction of online reviews [1][2][3][4]23]. Chen et al [1] designed a word-level gating mechanism to represent the relative importance of each word.…”

Section: Deep Neuralmentioning

confidence: 99%

“…Fan et al [3] proposed a multitask paradigm to predict the star ratings of reviews and to identify the helpful reviews more accurately. They also utilized the metadata of the target product in addition to the textual content of a review to better represent a review [4]. The methods summarized above are representative of the research progress in review helpfulness prediction.…”

Section: Deep Neuralmentioning

confidence: 99%

“…The methods summarized above are representative of the research progress in review helpfulness prediction. Sentiment and product information have been explored previously [4,7,10,15]. With respect to sentiment, Martin and Pu [15] extracted emotional words from review text to serve as important parameters for helpfulness prediction.…”

Section: Deep Neuralmentioning

confidence: 99%

“…Previous work typically derived useful information from different sources, such as a review content [6,15,28], metadata [4,15,17], and context [14,20,25]. However, such features are extracted from each source independently, without considering interactions.…”

Section: Introductionmentioning

confidence: 99%

“…With these expectations in mind, the customer pays special attention to those aspects of the review text that they find most salient. There have been earlier efforts [4,6,13] at capturing useful information from a review by considering product information. However, the unique aspects of each product (different levels of importance of attributes, evaluation standard, etc.)…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An Attention Model of Customer Expectation to Improve Review Helpfulness Prediction

Farkas

et al. 2020

Lecture Notes in Computer Science

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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.

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