An effective approach for mining mobile user habits

Abstract

The user interaction with the mobile device plays an important role in user habit understanding, which is crucial for improving context-aware services. In this paper, we propose to mine the associations between user interactions and contexts captured by mobile devices, or behavior patterns for short, from context logs to characterize the habits of mobile users. Though several state-of-the-art studies have been reported for association mining, they cannot apply to behavior pattern mining due to the unbalanced occurrences of contexts and user interaction records. To this end, we propose a novel approach for behavior pattern mining which takes context logs as time ordered sequences of context records and takes into account the co-occurrences of contexts and interaction records in the whole time ranges of contexts. Moreover, we develop an Apriori-like algorithm for behavior pattern mining and improve the original algorithm in terms of efficiency by introducing the context hash tree. Last, we build a data collection system and collect the rich context data and interaction records of 50 recruited volunteers from their mobile devices. The extensive experiments on the collected real life data clearly validate the ability of our approach for mining effective behavior patterns.