Αποτελέσματα Αναζήτησης
13 Αυγ 2018 · In this post, we aim to create the optimal hotel recommendations for Expedia’s users that are searching for a hotel to book. We will model this problem as a multi-class classification problem and build SVM and decision tree in ensemble method to predict which “hotel cluster” the user is likely to book, given his (or her) search details.
Abstract. Using Naive Bayes Classifier for sentiment analysis on a dataset with 515K reviews on luxury European hotels, I have obtained a training accuracy of 93.5 percent, and a testing accuracy of 92.5 percent when predicting positive and negative reviews.
Advisor : Yuxiao Huang (yhvcs@rit.edu) --------------------------------------------------------------------------------------. The main file is called hotel_recommender.py which performs the following operations. - Data preparation & down-sampling. - Building a model. - Running of 4 machine learning algorithms.
In this project, the objective is to transform implicit information provided by users into explicit features for hotel recommendation system engine. There are two parts to this recommender engine using hotel attributes and reviews by users respectively to build two separate recommendation engine.
13 Φεβ 2021 · A hotel recommendation system aims to predict which hotel a user is most likely to choose from among all hotels. So to build this type of system which will help the user to book the best hotel out of all the other hotels.
15 Απρ 2023 · Learn how to create a semantic search engine using Python, machine learning, and Jupyter Notebooks. Enhance search results by understanding user intent and context with this step-by-step...
16 Αυγ 2023 · AI and machine learning work by analyzing vast amounts of data to identify patterns and trends. In the context of hotel search engines, these technologies can be used to provide more accurate predictions and personalized recommendations.
