Time-aware user profiling from personal service ecosystem

The tremendous growth of services available on the Internet makes user profiling an increasing important topic in web personalization. While profiling a user, one of the main challenges is that user preferences and interests usually change over time. It would be much appreciated, if the user profile could describe these changes. Such user profile is defined as time-aware user profile (TUP) in this paper. The TUP can delineate both dynamic user preferences and interests and evolutions of them, which illustrates that it can represent users more clearly and accurately. A TUP consists of a set of time slot-specific user profiles, which is defined as sub-time-aware user profile (sTUP). And a TUP is special for a user; then, its sTUPs separately represent preferences and interests of this user for different time slots. Constructing a TUP needs the constructions of its sTUPs. While constructing a sTUP from the user behavior logs for a time slot, in this paper, we present a novel learning model called personal service ecosystem (PSE) to delineate user preferences and interests naturally. Different from PSE, sTUP is the representation model. For a time slot, the sTUP is constructed based on the PSE recovered from the user behavior logs of this time slot. In terms of a TUP, two main approaches to analyze the evolutions of sTUPs in this TUP are further discussed. They are the evolution analysis of various margins between every two neighboring sTUPs called EA-VM and the evolution analysis of different sTUPs on raw data defined as EA-RD. EA-VM mainly focuses on the fine-grained evolution between two neighboring time slots by analyzing these various margins. It can be further divided into the clustering method and the distribution analysis approach. EA-RD chiefly pays attentions to the overall evolution of such sTUPs. By a comprehensive survey on the raw data of sTUPs, we summarize six interesting overall evolution patterns, including refugee pattern, periodic pattern, stable pattern, fluctuant pattern, emergency pattern and zombie pattern. Eventually, a systematic empirical study based on our collection data has been conducted, where smartphone users are taken as examples to illustrate our proposed models and approaches. And experimental results highlight the superiority of these approaches.