Towards more accurate and intelligent recommendation systems
Timo Kahara, Keijo Haataja, Pekka Toivanen · 2013
In this paper, a comparative analysis of current Recommendation Systems (RSs) is provided. In addition, we propose a new efficient Utility-Based Filtering (UBF) and Knowledge-Based Filtering (KBF) hybrid approach for calculating the utility value of each object to be recommended. The proposed system is anticipated to be at least streamlined, straightforward, and less burdening solution for recommending variety of personalized services for the user. The purpose of this paper is to help RS designers to implement more accurate and intelligent recommendation engines as well as convince them of the benefits of our new smarter system that combines best of the UBF and KBF methods, thus resulting in one unique and effective hybrid system. Moreover, some new ideas that will be used in our future research work are proposed.