NP-miner: A real-time recommendation algorithm by using web usage mining

Web usage mining is widely applied in various areas, and dynamic recommendation is one web usage mining application. However, most of the current recommendation mechanisms need to generate all association rules before recommendations. This takes lots of time in offline computation, and cannot provide real-time recommendations for online users. This study proposes a Navigational Pattern Tree structure for storing the web accessing information. Besides, the Navigational Pattern Tree supports incremental growth for immediately modeling web usage behavior. To provide real-time recommendations efficiently, we develop a Navigational Pattern mining (NP-miner) algorithm for discovering frequent sequential patterns on the proposed Navigational Pattern Tree. According to historical patterns, the NP-miner scans relevant sub-trees of the Navigational Pattern Tree repeatedly for generating candidate recommendations. The experiments study the performance of the NP-miner algorithm through synthetic datasets from real applications. The results show that the NP-miner algorithm can efficiently perform online dynamic recommendation in a stable manner.