In electronic commerce, recommender systems are popularly being used to help enterprises for satisfying customers' individually diverse preferences. These systems learn about user preferences over time and automatically suggest products that fit the learned model of user preferences. In tradition, recommendations are provided to customers based on purchase probability and customers' preferences, without considering the profitability factor for sellers. This work presents a new profitability-based recommender system, HPRS (Hybrid Perspective Recommender System), which attempts to integrate the profitability factor into the traditional recommender systems. Comparisons between our proposed system and traditional system which only considers the purchase probability clarify the advantages of our system. The experimental results show that the proposed HPRS can increase profit from cross-selling without compromising recommendation accuracy.