Abstract
Normally, individuals use smartphones for a variety of purposes like photography, schedule planning, playing games, and so on, apart from benefiting from the core tasks of call-making and short messaging. These services are sources of personal data generation. Therefore, any application that utilises personal data of a user from his/her smartphone is truly a great witness of his/her interests and this information can be used for various personalised services. In this paper, we present Lifestyle Pattern MIning (LPaMI), which is a personalised application for mining the lifestyle patterns of a smartphone user. LPaMI uses the personal photograph collections of a user, which reflect the day-to-day photos taken by a smartphone, to recognise scenes (called objects of interest in our work). These are then mined to discover lifestyle patterns. The uniqueness of LPaMI lies in our graph-based approach to mining the patterns of interest. Modelling of data in the form of graphs is effective in preserving the lifestyle behaviour maintained over the passage of time. Graph-modelled lifestyle data enables us to apply variety of graph mining techniques for pattern discovery. To demonstrate the effectiveness of our proposal, we have developed a prototype system for LPaMI to implement its end-to-end pipeline. We have also conducted an extensive evaluation for various phases of LPaMI using different real-world datasets. We understand that the output of LPaMI can be utilised for variety of pattern discovery application areas like trip and food recommendations, shopping, and so on.
Highlights
Smartphone usage is common for a variety of auxiliary services like photography, game playing, schedule planning, and so on
We present Lifestyle Pattern MIning (LPaMI), which was introduced and demonstrated at the World IT Show organised in COEX Mall, Seoul, South Korea from 24 to 27 May 2017 as part of G-ITRC project from Kyung Hee University, South Korea
To mine the lifestyle patterns through the photo collections found on user smartphones, we present a prototype application, LPaMI, that recognises the objects of interest (OOIs) via state-of-the-art scene recognition techniques
Summary
Smartphone usage is common for a variety of auxiliary services like photography, game playing, schedule planning, and so on. Utilisation of these services generates large amounts of data with respect to personal use for individuals. This data contains the use patterns of various applications [1,2,3,4,5] and helps to understand personality traits [6]. We find that utilizing the data from smartphone usage can help facilitate learning of the lifestyle patterns/behaviour of a user
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