With the explosive growth of available data, recommender systems have become an essential tool to ease users with their decision-making procedure. One of the most challenging problems in these systems is the data sparsity problem, i.e., lack of sufficient amount of available users’ interactions data. Recently, cross-network recommender systems with the idea of integrating users’ activities from multiple domain were presented as a successful solution to address this problem. However, most of the existing approaches utilize users’ past behaviour to discover users’ preferences on items’ patterns and then suggest similar items to them in the future. Hence, their performance may be limited due to ignore recommending divers items. Users are more willing to be recommended with a variety set of items not similar to those they preferred before. Therefore, diversity plays a crucial role to evaluate the recommendation quality. For instance, users who used to watch comedy movie, may be less likely to receive thriller movie, leading to redundant type of items and decreasing user’s satisfaction. In this paper, we aim to exploit user’s personality type and incorporate it as a primary and enduring domain-independent factor which has a strong correlation with user’s preferences. We present a novel technique and an algorithm to capture users’ personality type implicitly without getting users’ feedback (e.g., filling questionnaires). We integrate this factor into matrix factorization model and demonstrate the effectiveness of our approach, using a real-world dataset.