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摘 要
随着互联网和移动通信迅猛发展，电子商务强势崛起，越来越多的人倾向于网上消费。如何从海量的互联网数据中筛选出用户感兴趣的信息成为了全球互联网用户潜在的问题，推荐系统(Recommendation System)技术通过搜索大量动态生成的信息来为用户提供个性化的内容和服务来解决这个问题。 推荐系统作为一种信息过滤方式，试图预测用户的偏好兴趣和对物品的评价。近年来，频繁活跃的互联网用户在消费信息的同时也产出了海量的原创内容。本文的主要研究工作是深度挖掘用户原创的评论内容，分析出用户和物品的特征，进而进行评分预测。 评论(抗氧化植物素Comment保健水杯)指人对于事物做出的客观叙述，反映了人的主观感受。基于用户的文本评论数据，本文的主要研究工作如下：
首先，从互联网上采集包含有用户、物品和用户文本评论的数据。该数据集来源于大众点评网。然后对评论文本进行分词，用词向量对其进行数学表达，形成主题词的分布表。
最后，基于用户文本用评论主题词进行评分预测，通过线性回归模型和改进的协同过滤算法预测评分，最终的实验结果表明，预测的评分客观准确，同时组合的预测算法效果更优。
关键词：推荐系统；用户评论；线性回归；评分预测
Abstract自动化机械手臂
With the rapid development of the Internet and mobile communications, and the strong rise of e-commerce, more and more people tend to spend online.火焰检测 How to filter the informa
tion that users are interested in from the massive Internet data has become a potential problem for global Internet users. Recommendation systems solve this problem by searching through large volume of dynamically generated information to provide users with personalized content and services.水培鱼缸>水利u型槽成型机
The recommendation system serves as an information filtering method that attempts to predict the user's preference for interest and the evaluation of the item. In recent years, frequent and active Internet users have also produced massive amounts of original content while consuming information. The main research work of this paper is to deeply mine user-originated commentary content, analyze the characteristics of users and items, and then make score predictions.
Comment reflects people’s subjective feelings. Based on the user's text review data, the main research work of this paper is as follows:
First, data containing user, item, and user text reviews is collected from the Internet. This dataset comes from the Dianping’s website. Then, the comment text is segmented and m
athematically expressed by the word vector. Then the text of the comment is segmented and expressed mathematically by the word vector to form the distribution table of the topic word.
Finally, based on the user's comment, the scores are predicted by the subject headings, and the linear regression model and the improved collaborative filtering algorithm are used to predict the scores. The final experimental results show that the predicted scores are objective and accurate, and the combined rating prediction algorithm is more effective.
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