Understanding customer malling behavior in an urban shopping mall using smartphones

Abstract This paper presents a novel customer malling behavior modeling framework for an urban shopping mall. As an automated computing framework using smartphones, it is designed to provide comprehensive understanding of customer behavior. We prototype the framework in a real-world urban shopping mall. Development consists of three steps; customer data collection, customer trace extraction, and behavior model analysis. We extract customer traces from a collection of 701-hour sensor data from 195 in-situ customers who installed our logging application at Android Market. The practical behavior model is created from the real traces. It has a multi-level structure to provide the holistic understanding of customer behavior from physical movement to service semantics. As far as we know, it is the first work to understand complex customer malling behavior in offline shopping malls

A context-sensitive conceptual framework for activity modeling

Human motion trajectories, however captured, provide a rich spatiotemporal data source for human activity recognition, and the rich literature in motion trajectory analysis provides the tools to bridge the gap between this data and its semantic interpretation. But activity is an ambiguous term across research communities. For example, in urban transport research activities are generally characterized around certain locations assuming the opportunities and resources are present in that location, and traveling happens between these locations for activity participation, i.e., travel is not an activity, rather a mean to overcome spatial constraints. In contrast, in human-computer interaction (HCI) research and in computer vision research activities taking place along the way, such as reading on the bus, are significant for contextualized service provision. Similarly activities at coarser spatial and temporal granularity, e.g., holidaying in a country, could be recognized in some context or domain. Thus the context prevalent in the literature does not provide a precise and consistent definition of activity, in particular in differentiation to travel when it comes to motion trajectory analysis. Hence in this paper, a thorough literature review studies activity from different perspectives, and develop a common framework to model and reason human behavior flexibly across contexts. This spatio-temporal framework is conceptualized with a focus on modeling activities hierarchically. Three case studies will illustrate how the semantics of the term activity changes based on scale and context. They provide evidence that the framework holds over different domains. In turn, the framework will help developing various applications and services that are aware of the broad spectrum of the term activity across contexts

Revisitation analysis of smartphone app use

We present a revisitation analysis of smartphone use to investigate the question: do smartphones induce usage habits? We analysed three months of application launch logs from 165 users in naturalistic settings. Our analysis reveals distinct clusters of applications and users which share similar revisitation patterns. However, we show that much of smartphone usage on a macro-level is very similar to web browsing on desktops, and thus argue that smartphone usage is driven by innate service needs rather than technology characteristics. On the other hand, on a micro-level we identify unique characteristics in smartphone usage, and we present a rudimentary model that accounts for 92 % in the variability of our smartphone use. Author Keywords Revisitation, smartphone use, habits, user behaviou

Where Does the Time Go: Smartphone Use Among Immigrant Mothers Born in the English-Speaking Caribbean

This ethnographic study investigates the ways in which smartphone use shape the daily routines of seven mothers who immigrated to Montreal from the English-speaking Caribbean. Using a combination of empirical data collection through a use-tracking app installed on participants’ smartphones and open-ended interviews, the paper argues that the pervasive use of smartphones in these mothers’ routines creates conflict with children while simultaneously providing mothers with a valuable outlet for socialization, identity creation and the maintenance of community ties. Evidence found in this study also suggests that media literacy is a relevant concern for women belonging to this population. One of the roles of a “good” mother is media gatekeeper, and while most of the participants in this study subscribe to this belief, most of them also have very little working knowledge about the media landscape in which they inhabit, and in some cases very little ability to decode the meaning and function of basic media products. For these reasons, media literacy among this population emerged as both an area of inquiry and a possible area of further study or outreach

An integrated mobile content recommendation system

Many features have been added to mobile devices to assist the user's information consumption. However, there are limitations due to information overload on the devices, hardware usability and capacity. As a result, content filtering in a mobile recommendation system plays a vital role in the solution to this problem. A system that utilises content filtering can recommend content which matches a user's needs based on user preferences with a higher accuracy rate. However, mobile content recommendation systems have problems and limitations related to cold start and sparsity. The problems can be viewed as first time connection and first content rating for non-interactive recommendation systems where information is insufficient to predict mobile content which will match with a user's needs. In addition, how to find relevant items for the content recommendation system which are related to a user's profile is also a concern. An integrated model that combines the user group identification and mobile content filtering for mobile content recommendation was proposed in this study in order to address the current limitations of the mobile content recommendation system. The model enhances the system by finding the relevant content items that match with a user's needs based on the user's profile. A prototype of the client-side user profile modelling is also developed to demonstrate the concept. The integrated model applies clustering techniques to determine groups of users. The content filtering implemented classification techniques to predict the top content items. After that, an adaptive association rules technique was performed to find relevant content items. These approaches can help to build the integrated model. Experimental results have demonstrated that the proposed integrated model performs better than the comparable techniques such as association rules and collaborative filtering. These techniques have been used in several recommendation systems. The integrated model performed better in terms of finding relevant content items which obtained higher accuracy rate of content prediction and predicted successful recommended relevant content measured by recommendation metrics. The model also performed better in terms of rules generation and content recommendation generation. Verification of the proposed model was based on real world practical data. A prototype mobile content recommendation system with client-side user profile has been developed to handle the revisiting user issue. In addition, context information, such as time-of-day and time-of-week, could also be used to enhance the system by recommending the related content to users during different time periods. Finally, it was shown that the proposed method implemented fewer rules to generate recommendation for mobile content users and it took less processing time. This seems to overcome the problems of first time connection and first content rating for non-interactive recommendation systems