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Abstract
As the e-commerce market grows worldwide, personalized recommendation services have become essential to users’ personalized items or services. They can decrease the cost of user information exploration and have a positive impact on corporate sales growth. Recently, many studies have been actively conducted using reviews written by users to address traditional recommender system research problems. However, reviews can include content that is not conducive to purchasing decisions, such as advertising, false reviews, or fake reviews. Using such reviews to provide recommendation services can lower the recommendation performance as well as a trust in the company. This study proposes a novel review of the helpfulness-based recommendation methodology (RHRM) framework to support users’ purchasing decisions in personalized recommendation services. The core of our framework is a review semantics extractor and a user/item recommendation generator. The review semantics extractor learns reviews representations in a convolutional neural network and bidirectional long short-term memory hybrid neural network for review helpfulness classification. The user/item recommendation generator models the user’s preference on items based on their past interactions. Here, past interactions indicate only records in which the user-written reviews of items are helpful. Since many reviews do not have helpfulness scores, we first propose a helpfulness classification model to reflect the review helpfulness that significantly impacts users’ purchasing decisions in personalized recommendation services. The helpfulness classification model is trained about limited reviews utilizing helpfulness scores. Several experiments with the Amazon dataset show that if review helpfulness information is used in the recommender system, performance such as the accuracy of personalized recommendation service can be further improved, thereby enhancing user satisfaction and further increasing trust in the company.
Highlights
As the e-commerce market overgrows worldwide with the development of information technology and the popularization of mobile devices, various types of products continue to be released [1,2]
To evaluate Convolutional Neural Network (CNN)–Bi-directional Long Short-Term Memory (BiLSTM) hybrid model classification performance in this study, we experimented with DS1 and adopted Accuracy, Precision, Recall, and F1-score as metrics
To evaluate the prediction performance of the proposed recommendation framework, we experimented with DS2 and adopted Mean Absolute Error (MAE) and
Summary
As the e-commerce market overgrows worldwide with the development of information technology and the popularization of mobile devices, various types of products continue to be released [1,2]. Users face a time-consuming information overload problem in the purchasing decision-making process. The issue of information overload multiplies because the user experiences the product indirectly online. Personalized recommendation services have been becoming important in providing personalized items or services to users. Global e-commerce companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to help users make purchasing decisions [3,4,5]. They can decrease the cost of user information exploration and have a positive impact on corporate sales growth. Amazon generates 35% of its total revenue from items recommended to users through personal recommendation services [6]
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