A multi-stage collaborative filtering approach for mobile recommendation

Location-based and personalized services are the key factors for promoting user satisfaction. However, most service providers did not consider the needs of mobile user in terms of their location and event-participation. Consequently, the service provider may lose the chance for better service and profit. In this paper, we present a Multi-stage Collaborative Filtering (MSCF) process to provide event recommendation based on mobile user's location. To achieve this purpose, the Collaborative Filtering (CF) technique is employed and the Adaptive Resonance Theory (ART) network is applied to cluster mobile users according to their personal profile. Sequential pattern mining is, then, used to discover the correlations between events for recommendation. The MSCF is designed not only to recommend for the old registered mobile user (ORMU), but also to handle the cold-start problem for new registered mobile user (NRMU). This research is designed to achieve the followings. (1) To present a personalized event recommendation system for mobile users. (2) To discover mobile users' moving patterns. (3) To provide recommendations based on mobile users' preferences. (4) To overcome the cold-start problem for new registered mobile user. The experimental results of this research show that the MSCF is able to accomplish the above purposes and shows better outcome for cold-start problem when comparing with user-based CF and item-based CF.

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