Long-term Product Rating Prediction Based on Users' Short-term Multiple Ratings

Parnian Zare, M. Mehrabi
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Abstract

Ratings and product reviews could be considered as one of the main features determining the quality of a product in online store systems, especially in deciding whether to place a product as part of an online store's inventory. online vendors are often attracted by product reviews and ratings. However, when the average product rating observed based on a small number of user ratings, the decision maker may not be certain about choosing that product, even if it has a fairly high rate. Long-term rating predictions would help online vendors to identify products and advertise their websites by choosing potential ones. In this paper machine learning approach utilizing linear regression model is used to predict long-term product rate. The model evaluated using the Datasheet of the Amazon Online Store website,1996 to 2014. Keywords : Rating, Long-term Prediction, Machine Learning Algorithm, Linear Regression. DOI : 10.7176/JIEA/9-4-04 Publication date :June 30 th 2019
基于用户短期多重评级的长期产品评级预测
评级和产品评论可以被认为是决定在线商店系统中产品质量的主要特征之一,特别是在决定是否将产品作为在线商店库存的一部分时。网上卖家通常会被产品评论和评级所吸引。然而,当基于少量用户评级观察到的平均产品评级时,决策者可能不确定是否选择该产品,即使它的比率相当高。长期评级预测将帮助在线供应商识别产品,并通过选择潜在的产品为他们的网站做广告。本文利用线性回归模型的机器学习方法来预测长期产出率。该模型使用1996年至2014年亚马逊在线商店网站的数据表进行评估。关键词:评级,长期预测,机器学习算法,线性回归。出版日期:2019年6月30日
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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