A Novel Approach towards Tourism Recommendation System with Collaborative Filtering and Association Rule Mining

Abstract

In the tourism recommendation system, the number of users and items is very large. But traditional recommendation system uses partial information for identifying similar characteristics of users. Collaborative filtering is the primary approach of any recommendation system. It provides a recommendation which is easy to understand. It is based on similarities of user opinions like rating or likes and dislikes. So the recommendation provided by collaborative cannot be considered as quality recommendation. Recommendation after association rule mining is having high support and confidence level. So that will be considered as strong recommendation. The hybridization of both collaborative filtering and association rule mining can produce strong and quality recommendation even when sufficient data are not available. This paper combines recommendation for tourism application by using a hybridization of traditional collaborative filtering technique and data mining techniques.