Collaborative filtering (CF) recommender systems have been used in various application domains to solve the information-overload problem. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques to improve the quality of recommendation. Some researchers have proposed rating-based trust models to derive the trust values based on users' past ratings of items, or based on explicitly specified relations (e.g. friends) or trust relationships. The rating-based trust model may not be effective in CF recommendations, due to unreliable trust values derived from very few past rating records. In this work, we propose a hybrid personal trust model which adaptively combines the rating-based trust model and explicit trust metric to resolve the drawback caused by insufficient past rating records. Moreover, users with similar preferences usually form a group to share items (knowledge) with each other, and thus users' preferences may be affected by group members. Accordingly, group trust can enhance personal trust to support recommendation from the group perspective. Eventually, we propose a recommendation method based on a hybrid model of personal and group trust to improve recommendation performance. The experiment result shows that the proposed models can improve the prediction accuracy of other trust-based recommender systems.