利用机器学习技术改进酒店推荐系统

Er. Abdul Hafiz, Narinderpal Kaur
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引用次数: 1

摘要

下面的报告展示了一个基于用户位置的酒店建议系统是如何实现的。采用多种方法提高准确率;许多学者在这一领域工作并实现了算法,如随机森林,朴素贝叶斯,链接预测,J48。基本上,使用随机森林算法来获得更好的预测准确率。在时间和金钱方面,及时选择酒店的特定位置至关重要。一般来说,价格和用户评论被用作选择酒店的因素,而那些酒店应该既具有成本效益,又具有较高的评分。另一方面,随着该地区酒店数量的增加,顾客很难轻松找到想要的酒店。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved Hotel Recommendation System Using Machine Learning Technique
The report below shows how a hotel suggestion system based on the user's location was implemented. Many methods are utilized to raise the accuracy percentage; numerous academics have worked in this field and implemented algorithms, like random forest, Naive Bayes, Link prediction, J48. Basically, random forest algorithm is used to attain a better rate of prediction accuracy. In terms of both time and money, it's critical to choose a hotel promptly for a specific location. In general, pricing and user reviews have been used as factors for picking hotels, with the hotels that are supposed to be both cost effective and high rating score. On the other hand, as the number of hotels in the area grows, Customers have difficulty easily locating the desired hotel.
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