Comparing accuracy of cosine-based similarity and correlation-based similarity algorithms in tourism recommender systems
Elnaz Bigdeli, Zeinab Bahmani · 2008
Recommender system has a long history as a successful application in artificial intelligence. A growth in the number of products, which has been offered by different e-commerce platforms, leads to a technology which can help customers to choose and buy products. Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. This paper describes some algorithms designed for this task including cosine-based similarity algorithm and correlation-based similarity algorithm. The predictive accuracy of various methods in tourism recommender domains is compared. On the other hand, we have designed and implemented a recommender system in e-tourism in order to compare performance of these algorithms. Finally, we conclude that correlation based similarity algorithm acts better than Cosine based similarity algorithm.