Abstract
Today, recommendation system has been globally adopted as the most effective and reliable search engine for knowledge extraction in the field of education, economics and scientific research. Collaborative filtering is a proven techniques used in recommender system to make predictions or recommendations of the unknown preferences for users based on the known user preferences. In this paper, collaborative filtering task and their challenges are explored, study the different recommendation techniques and evaluate their performance using different metrics.
Highlights
Recommendation has become the most important tool used to change the method of communication between web users and web sites
Recommender system has been widely used in the field of economics, education and scientific research [1]
Collaborative filtering as a classic filtering algorithm for the development of recommender system is divided into item based collaborative filtering and user based collaborative filtering
Summary
Recommendation has become the most important tool used to change the method of communication between web users and web sites. A cold start problem occurs when a new user or item is added to the system In this situation it is difficult to find a similar user as there is no enough information about the user. A reduced coverage occurs due to a small number of ratings when compared to the large number of items/products in the system In this case the recommender system may not be able to generate a prediction for the items. Neighbor transitivity may reduce the effectiveness of the recommendation if the databases are sparse In this case users with similar taste may not be identified [2]. When the number of users and items increases, the traditional collaborative filtering algorithms undergoes a serious scalability problem as the computational resources exceeds practical or acceptable level [4].
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