The purpose of this paper is to develop a novel and flexible recommender system based on usage patterns and keyword preferences using collaborative filtering (CF) and content‐based filtering (CBF).
The proposed system analyzes data captured from the navigational and behavioral patterns of users and estimates the popularity and similarity levels of a user's clicked content. Based on this information, content is recommended to each user using recommendation methods such as CF and CBF. To assess the effectiveness of the proposed approach, an empirical study was conducted by constructing an experimental news site.
The results of the experimental study clearly show that the proposed hybrid method is superior to conventional methods that use only CF or CBF.
The above findings are based on data captured from a relatively small experimental site, and they require further verification using various actual content sites. A promising area for future research may be the application of the proposed approach to making recommendations in user‐created content environments, such as blog sites and video upload sites, where users can actively participate as both writers and readers.
Unlike the most research on recommender systems, this is the first study to analyze user usage patterns and thereby determine appropriate recommendation algorithms for each user. The proposed recommender system provides greater prediction accuracy than conventional systems.
