基于用户评分推荐餐厅的机器学习模型

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引用次数: 0

摘要

然而,人们通常只是用“restaurant”这个词来搜索一家餐馆,而“restaurant”这个词对不同的人有不同的含义。对亚洲人来说,它可以指“中餐馆”或“泰国餐馆”。如何根据人们的偏好正确地解释搜索请求是一个挑战。建立一个基于注册用户活动历史的机器学习模型可以解决这个问题。本研究使用的活动历史记录是来自用户的评论和评级。这个项目引入了一个数据处理管道,它使用注册用户的评论为每个注册用户生成一个机器学习模型。该项目还定义了一个架构,该架构使用生成的机器学习模型来支持餐厅搜索的实时个性化推荐和推荐餐厅的美食类型。最后,本项目旨在开发一个良好的机器学习模型,考虑了不同的协同过滤方法来使用用户评级来预测餐馆。斜率1,k近邻算法和多类支持向量机分类是本项目将要考虑的一些协作方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Machine Learning Model for Recommending Restaurants based on User Ratings
However, oftentimes people just search a restaurant by using word “restaurant”, while the word “restaurant” means differently to different individuals. For an Asian, it can mean a “Chinese restaurant” or “Thai restaurant”. How to correctly interpret search requests based on people’s preference is a challenge. Building a machine-learning model based on activity history of a registered user can solve this problem. The activity histories used by this research are reviews and ratings from users. This project introduces a data processing pipeline, which uses reviews from registered users to generate a machine-learning model for each registered user. This project also defines an architecture, which uses the generated machine-learning models to support real-time personalized recommendations for restaurant searching and type of foods good at those recommended restaurants. Finally, this project aims to develop a good machine learning model, different collaborative filtering methodologies are considered to predict restaurants using user ratings. Slope One, k-Nearest Neighbors algorithm and multiclass SVM classification are some of the collaborating methodologies are going to consider in this project.
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