Recommender systems are gaining a great importance with the emergence of e-Learning and business on the internet. These recommender systems help users in making decision by suggesting products and services that satisfy the users’ required goals and profile preferences. Collaborative filtering and content- based recommendation are two fundamental methods used in hybrid approach based recommender systems. Although, both methods have their own advantages, they fail in some situations such as the ‘user cold-start’ where new users or items are added in the system. In this paper, we propose a new hybrid approach that combines k-nearest neighborhood collaborative filtering and content-based collaborative filtering in goal-based hybrid filtering for personalized recommender system. The purpose of combining these approaches is to overcome new-user cold-start profile content filtering accuracy issue, its experimental setup and initial results. Moreover, we show how our approach deals with the user-to-user cold-start problem by incorporating user profiling characteristics collaboratively.
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