Effective personalized recommendation based on time-framed navigation clustering and association mining

Personalized recommendation by predicting user-browsing behavior using association-mining technology has gained much attention in web personalization research area. However, the resulting association patterns did not perform well in prediction of future browsing patterns due to the low matching rate of the resulting rules and users' browsing behavior. This research proposes a new personalized recommendation method integrating user clustering and association-mining techniques. Historical navigation sessions for each user are divided into frames of sessions based on a specific time interval. This research proposes a new clustering method, called HBM (Hierarchical Bisecting Medoids Algorithm) to cluster users based on the time-framed navigation sessions. Those navigation sessions of the same group are analyzed using the association-mining method to establish a recommendation model for similar students in the future. Finally, an application of this recommendation method to an e-learning web site is presented, including plans of recommendation policies and proposal of new efficiency measures. The effectiveness of the recommendation methods, with and without time-framed user clustering, are investigated and compared. The results showed that the recommendation model built with user clustering by time-framed navigation sessions improves the recommendation services effectively.