Cuisine Recommendation, Classification and Review Analysis using Supervised Learning

Aruna Pavate, A. Chaudhary, P. Nerurkar, Priti Mishra, Mansi Shah
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引用次数: 1

Abstract

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.
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基于监督学习的菜肴推荐、分类和评论分析
为了帮助业务的增长,有必要对客户偏好进行详细的分析,以及对销售,产品购买和向用户推荐合适的内容进行分析。从产品推荐到内容推荐,有许多推荐系统可用。本文提出了一种基于现实世界中餐馆相关评论的膳食分类和推荐系统。现在每天都有大量的选择供用户订购他们的食物。从购物到娱乐,有很多推荐系统可用。烹饪是一个有很大机会建议顾客选择的食物的领域,这有助于节省他们的精力、时间和金钱。本文提出了利用SVM监督学习算法进行餐厅点评分析和美食推荐,并对系统的功能进行了分析。该方法在实际数据集上进行了实现和评估,实验结果表明,该方法的平均精度、召回率和f1得分在91%左右,表明了系统在推荐食物方面的有效性。
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