论文标题
个性化的评论排名改进购物者的决策：一种基于任期频率的方法
Personalized Review Ranking for Improving Shopper's Decision Making: A Term Frequency based Approach
论文作者
论文摘要
用户生成的评论是购物者决策过程中的关键参考。此外，它们改善了产品销售并验证整个网站的声誉。因此，设计评论的排名方法很重要，以帮助购物者迅速做出明智的决策。但是，评论排名有其独特的挑战。首先，没有审查的相关标签。对购物者A的相关评论可能与购物者B无关。第二，由于购物者无法单击评论，因此我们无法获得相关反馈。最终，由于对不同用户的相关性标准的可变性，评论排名缺乏地面真相。在本文中，我们旨在应对帮助用户找到他们可能对客户评论海洋感兴趣的信息的挑战。使用UCSD收集和组织的Amazon Customer评论数据集，我们首先根据用户的个人网络跟踪，最近的购物历史记录和以前的评论构建了用户配置文件，将用户资料纳入了我们的排名算法，并将更高的评论分配给了解决个人购物者在最大范围内的个人购物者关注的评论。此外，我们利用用户资料来推荐基于评论文本的产品。我们基于经验评估和审查分数的数值评估评估了模型。两种评估方法的结果都揭示了顶级评论质量以及1000多种产品的用户满意度的显着提高。我们的基于评论的推荐系统还表明，用户可以查看和喜欢我们推荐的产品。我们的工作显示了开发排名方法的基本步骤，该方法从特定最终用户的偏好中学习。
User-generated reviews serve as crucial references in shopper's decision-making process. Moreover, they improve product sales and validate the reputation of the website as a whole. Thus, it becomes important to design reviews ranking methods that help shoppers make informed decisions quickly. However, reviews ranking has its unique challenges. First, there is no relevance labels for reviews. A relevant review for shopper A might not be relevant to shopper B. Second, since shoppers cannot click on reviews, we have no ways of getting relevance feedback. Eventually, reviews ranking suffers from the lack of ground truth due to the variability in the standard of relevance for different users. In this paper, we aim to address the challenges of helping users to find information they might be interested in from the sea of customer reviews. Using the Amazon Customer Reviews Dataset collected and organized by UCSD, we first constructed user profiles based on user's personal web trails, recent shopping history and previous reviews, incorporated user profiles into our ranking algorithm, and assigned higher ranks to reviews that address individual shopper's concerns to the largest extent. Also, we leveraged user profiles to recommend products based on reviews texts. We evaluated our model based on both empirical evaluations and numerical evaluations of review scores. The results from both evaluation methods reveal a significant increase in the quality of top reviews as well as user satisfaction for over 1000 products. Our reviews based recommendation system also suggests that there's a large chance of user viewing and liking the product we recommend. Our work shows the basic steps of developing a ranking method that learns from a particular end-user's preferences.
