Tour Spot Recommendation System via Content-Based Filtering

Abstract

Online recommendation systems have gained prominence. The online recommendation systems provide a better and faster way to choose Tour Spots for traveling and transactions. Recommender systems are powerful emerging technologies that help consumers to find the best places for touring according to their interests. The recommendation system recommends the most appropriate destinations for the end users. Internet travel portals also compete with each other regularly by taking into consideration several characteristics. Recommendation systems are one of the best methods to raise income and attract consumers. Established systems retrieved irrelevant information that resulted in low customer satisfaction and disappointment of customers. In this research, the Tour Spot recommendation system through content-based filtering is proposed and designed. It recommends the best picnic spot according to the budget and interest of the user. The proposed system improves the tourist experience and recommends the best place according to the things they would like; most significantly it is budget friendly as it suggests different places according to the interests and needs of the user. Data collection, including user information, consolidated user engagement reports, and tour spot data, is the primary challenge of implementing a travel recommendation system. User information originates mainly from information entered by the user during the registration process. When the system has the data of user likes and dislikes, then the system compares the user preferences to the dataset that the author makes for the tour recommendation systems in the dataset first identifies all the features that must be considered at the time of selection of travel destination. This Article also considers user likes and dislikes and the previous history of users to recommend a similar tour spot.