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Social groups in the form of different discussion forums are proliferating rapidly. Most of these forums have been created to exchange and share members’ knowledge in various domains. Members in these groups may need to use and retrieve other members’ knowledge. Therefore, recommender systems are one of the techniques which can be employed in order to extract knowledge based on the members’ needs and favorites. It is noteworthy that not only the users’ comments and posts can have valuable information, but also there are some other valuable information which can be obtained from social data; moreover, it could be extracted from relations and interactions among users. Hence, association rules mining techniques are one of the techniques which can be applied in order to extract more implicit data as input to the recommender system. Our objective in this study is to improve the performance of a hybrid recommender system by defining new hybrid rules. In this regard, for the first time, we have defined new hybrid rules by considering both users and posts’ content data. Each of the defined rules has been examined on an asynchronous discussion group in this study. In addition, the impact of the defined rules on the precision and recall values of the recommender system has been examined. We found that according to this impact, a classification of the defined rules can be considered and a number of weights can be assigned to each rule based on their impact and usability in the specific domain or application. It is noteworthy that the results of the experiments have been promising.

An Extended Hybrid Recommender System Based on Association Rules Mining in Discussion Groups