An Enhanced Approach for Tourism Recommendation System using Hybrid Filtering and Association Rule Mining
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 and content based 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 and content based filtering is used to provide
opinion for the new users profile. So the recommendation
provided by collaborative and content cannot be
considered as quality recommendation. Recommendation
after association rule mining is having high support and
confidence level. So that it will be considered as strong
recommendation. The hybridization of both hybrid
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 and content filtering techniques
and data mining techniques.
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