Objective: The present study was conducted with the aim of identifying and validating the characteristics of a course resource recommendation system for faculty members. The method of this research was an exploratory mixed type. In the qualitative part, with a systematic review approach (based on the Kitchenham and Charters, 2007 method) and meta-synthesis technique, 40 related studies and 20 domestic and international databases were reviewed and the basic concepts were categorized using content analysis. In the quantitative part, a researcher-made questionnaire with 57 items was designed based on a 5-point Likert scale. The statistical population included faculty members of universities in Fars province, 94 of whom were selected through purposive sampling. The data were analyzed using one-sample t-test and exploratory factor analysis in SPSS version 25 software. The research findings indicated that in the qualitative part, 57 basic concepts were extracted, which were categorized into 13 organizing themes and finally into 4 overarching themes in the thematic analysis process: (1) Course resource features, (2) User profile features, (3) Similarity measurement criteria, (4) Display and user interface features. In the quantitative part, all four features were significantly approved by the faculty members. The average of the “Display and user interface” (4.32) and “User profile” (4.24) features was higher than the desired level (4). The total variance extracted was 65.61%, which indicates that the identified features cover more than 61% of the features of a desirable recommender system. The results of the study showed that four categories of features are essential for a course resource recommendation system, including bibliographic features (title, author, subject, year of publication, language), publisher information (publisher, journal, conference), access information (media type, cost, citations, user comments), and teaching university information (list of universities, appropriate course and field, number of teaching sessions). User characteristics include experience (service history, teaching history, academic rank), expertise and field (field of teaching, field of study, orientation, level of study), and interests and hobbies (educational and research interests). Regarding similarity measurement criteria, there are two main approaches including collaborative filtering (suggestions based on shared citations, previous searches of similar users, similar user behavior) and content-based approach (other works of the same author, similar sources, keyword similarity) for similarity measurement. The user interface also includes the search form (simple, advanced, expert), search term suggestion (word roots, spelling correction, close phrases, semantic relationships), ranking of suggestions (based on year, relevance, number of times taught), and displaying user comments (number of times viewed and downloaded, possibility of feedback).