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 in order to improve recommendation quality. Some researchers have proposed rating-based trust models to derive trust values based on users' past ratings of items, or based on explicitly specified relations (e.g. friends) or trust relationships; however, 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; thus, users' preferences may be affected by group members. Accordingly, group trust can enhance personal trust to support recommendations from the group perspective. We then propose a recommendation method based on a hybrid model of personal and group trust to improve recommendation performance. The experimental results show that the proposed models can improve the prediction accuracy of other trust-based recommender systems.