Recommender systems are applications to retrieve useful information from large amount of online data to assist users in discovering interesting items/products in the system. Collaborative filtering, content-based filtering, demographics-based filtering and hybrid approach are main approaches to realize recommendation systems. Most of the existing algorithms use a single approach to deal with recommendation problems. Besides, traditional recommendation approaches mainly deal with single dyadic

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... lationships between users and items whereas data in real world are generally conceptualized in terms of objects and relations between them. Recommender systems based on Probabilistic Relational Model (PRM) 1,2 , a framework for learning probabilistic models from relational data, have tried to address this issue. However, existing PRM-based recommendation algorithms do not fit into our context where we are struggling with the contradictory situation of a real-world application that requires building a personalized recommender when no user profile exists. Therefore, we propose a novel approach to build a personalized PRM-based recommendation model with the help of users' preferences on decision making criteria. Using our approach, content-based, collaborative filtering as well as hybrid models can be achieved from the same PRM. Applying the model on a real-world data from a cold system, we show that our model is actually capable of personalizing recommendations in coldstart situation.