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Cuisine Recommendation, Classification and Review Analysis using Supervised Learning

机译:使用监督学习的菜系推荐,分类和审查分析

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To help in growth of businesses it is necessary to do detailed analysis about the customer preferences as well as analysis of sales, products purchase and suggestions of right contents to the user. There are many recommendations systems are available from product recommendation to content recommendations. This works presents a meal classification and recommendation system involving restaurant-related reviews obtained from the real world. Now a day a huge range of options are available for the user to order their foods. There are lots of recommendation systems are available from shopping to recreations. Cuisine is one territory where there is a major chance to suggest meal of customer's choices which helps to save their lot of efforts, time and money. In this work restaurant review analysis and cuisine recommendation proposed using SVM supervised learning algorithm and the functioning of the system analyzed. The proposed method implemented, evaluated on the real world data set and an experimental results gives an average precision, recall and F1-score around 91% which shows the effectiveness of the system in recommendation of meal.
机译:为了帮助企业的增长,有必要对客户偏好进行详细的分析,以及对用户的销售,产品购买和正确内容的建议进行详细的分析。产品建议提供了许多建议系,以供包含在内的内容建议。这项工作介绍了涉及从现实世界获得的餐馆相关审查的膳食分类和推荐系统。现在,用户可以为用户提供一系列巨大选择来订购食物。购物提供了许多推荐系统可从购物娱乐。菜式是一个领土,有一个主要的机会建议客户的选择,有助于挽救他们很多努力,时间和金钱。在这项工作中,使用SVM监督学习算法提出的审查分析和美食推荐和系统的运作分析。实施的方法,在现实世界数据集和实验结果上进行评估,召回和F1分数约为91%,这表明了系统在膳食推荐中的有效性。

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