Commenders: A recommendation procedure for online book communities

We propose a recommendation procedure for online book communities called “Commenders.” Its purpose is to enhance the effectiveness of community recommendation and also the satisfaction of individual members. The basic idea of our proposed approach is collaborative filtering (CF). It adapts a content-based (CB) filtering algorithm by representing items with keyword features. The proposed recommendation procedure consists of two steps. During the first step, Commenders finds neighbors using community preferences for books and their feature information, and then it generates a CF-based recommendation list. The second step removes irrelevant books from the CF-based list using the keyword preferences of individual members. Commenders is designed to reduce individual member dissatisfaction with the process of finding desired books within an online community. To evaluate the procedure, we built a prototype system and performed experiments. Our experimental results show that the proposed system offers higher quality recommendations than the traditional collaborative filtering system. The proposed system has consistently higher precision, and individual members are more satisfied using this system.