Modeling Users’ Behavior from Large Scale Smartphone Data Collection

Abstract

A large volume of research in ubiquitous systems has been devoted to using data, that has been sensed from users' smartphones, to infer their current high level context and activities. However, mining users' diverse longitudinal behavioral patterns, which can enable exciting new context-aware applications, has not received much attention. In this paper, we focus on learning and identifying such behavioral patterns from large-scale data collected from users' smartphones. To this end, we develop a unified infrastructure and implement several novel approaches for building diverse behavioral models of users. Examples of generated models include classifying users' semantic places and predicting their availability for accepting calls etc. We evaluate our work on real-world data of 200 users, from the DeviceAnalyzer dataset, consisting of 365 million data points and show that our algorithms and approaches can model user behavior with high accuracy and outperform existing approaches.

Designing Personalisation in a Environmental Recommendation System: Differences of AHP and Fuzzy AHP

“…As a result, recommendation systems for mobile phones have been developed. The main advantage of those systems is that they can make use of the large number of modalities for interaction between them and their environment [2]. Implicit information coming from Smartphone crowdsourcing can be combined with location and time specific data can further utilize software adaptation [8].…”

Enabling Proactivity in Context-aware Middleware Systems by means of a Planning Framework based on HTN Planning

“…In our previous work [1], we have addressed the first aforementioned capability of a proactive context-aware system -modeling and predicting user behavior, as part of the Rover II context-aware middleware [2,6]. In this paper, we address the second key capability required by a context-aware system to act proactively -acting autonomously without an explicit user request.…”