Enhanced Travel Planning System for Group of users using Hybrid Collaborative Filtering

Abstract

Objectives: This paper proposes a recommender system for travel planning build on the user personalized regard for both single and group of users by using the adaptable user interface and feedback mechanism. Methods: One of the major problems is that all the previous recommendation system based on traveling simply recommends the most common travel routes and places and they do not provide an appropriate and interested travel schedule to the user. Findings: First, the adaptable user interface is used to modify or remove the unsatisfied travel schedule of the user with the specific schedule. Next, the feedback mechanism provides better accuracy rate for the next schedule of the new user. Applications: The group recommendation is elicited out of the personal recommendation system which uses the scheduling reasoning algorithm to provide the user with the perfect travel plan. The proposed hybrid collaborative filtering technique for group recommendation system resolves the data sparsity problem. Along with this, the K-Means clustering algorithm is used to cluster the users and to group them according to their interest efficiently.