A user-centric evaluation of context-aware recommendations for a mobile news service

Traditional recommender systems provide personal suggestions based on the user’s preferences, without taking into account any additional contextual information, such as time or device type. The added value of contextual information for the recommendation process is highly dependent on the application domain, the type of contextual information, and variations in users’ usage behavior in different contextual situations. This paper investigates whether users utilize a mobile news service in different contextual situations and whether the context has an influence on their consumption behavior. Furthermore, the importance of context for the recommendation process is investigated by comparing the user satisfaction with recommendations based on an explicit static profile, content-based recommendations using the actual user behavior but ignoring the context, and context-aware content-based recommendations incorporating user behavior as well as context. Considering the recommendations based on the static profile as a reference condition, the results indicate a significant improvement for recommendations that are based on the actual user behavior. This improvement is due to the discrepancy between explicitly stated preferences (initial profile) and the actual consumption behavior of the user. The context-aware content-based recommendations did not significantly outperform the content-based recommendations in our user study. Context-aware content-based recommendations may induce a higher user satisfaction after a longer period of service operation, enabling the recommender to overcome the cold-start problem and distinguish user preferences in various contextual situations

Modeling Musical Mood From Audio Features, Affect and Listening Context on an In-situ Dataset

Musical mood is the emotion that a piece of music expresses. When musical mood is used in music recommenders (i.e., systems that recommend music a listener is likely to enjoy), salient suggestions that match a user’s expectations are made. The musical mood of a track can be modeled solely from audio features of the music; however, these models have been derived from musical data sets of a single genre and labeled in a laboratory setting. Applying these models to data sets that reflect a user’s actual listening habits may not work well, and as a result, music recommenders based on these models may fail. Using a smartphone-based experience-sampling application that we developed for the Android platform, we collected a music listening data set gathered in-situ during a user’s daily life. Analyses of our data set showed that real-life listening experiences differ from data sets previously used in modeling musical mood. Our data set is a heterogeneous set of songs, artists, and genres. The reasons for listening and the context within which listening occurs vary across individuals and for a single user. We then created the first model of musical mood using in-situ, real-life data. We showed that while audio features, song lyrics and socially-created tags can be used to successfully model musical mood with classification accuracies greater than chance, adding contextual information such as the listener’s affective state and or listening context can improve classification accuracies. We successfully classified musical arousal in a 2-class model with a classification accuracy of 67% and musical valence with an accuracy of 75%. Finally, we discuss ways in which the classification accuracies can be improved, and the applications that result from our models

Context-Aware Recommendation Systems in Mobile Environments

Nowadays, the huge amount of information available may easily overwhelm users when they need to take a decision that involves choosing among several options. As a solution to this problem, Recommendation Systems (RS) have emerged to offer relevant items to users. The main goal of these systems is to recommend certain items based on user preferences. Unfortunately, traditional recommendation systems do not consider the user’s context as an important dimension to ensure high-quality recommendations. Motivated by the need to incorporate contextual information during the recommendation process, Context-Aware Recommendation Systems (CARS) have emerged. However, these recent recommendation systems are not designed with mobile users in mind, where the context and the movements of the users and items may be important factors to consider when deciding which items should be recommended. Therefore, context-aware recommendation models should be able to effectively and efficiently exploit the dynamic context of the mobile user in order to offer her/him suitable recommendations and keep them up-to-date.The research area of this thesis belongs to the fields of context-aware recommendation systems and mobile computing. We focus on the following scientific problem: how could we facilitate the development of context-aware recommendation systems in mobile environments to provide users with relevant recommendations? This work is motivated by the lack of generic and flexible context-aware recommendation frameworks that consider aspects related to mobile users and mobile computing. In order to solve the identified problem, we pursue the following general goal: the design and implementation of a context-aware recommendation framework for mobile computing environments that facilitates the development of context-aware recommendation applications for mobile users. In the thesis, we contribute to bridge the gap not only between recommendation systems and context-aware computing, but also between CARS and mobile computing.<br /

Situation inference and context recognition for intelligent mobile sensing applications

The usage of smart devices is an integral element in our daily life. With the richness of data streaming from sensors embedded in these smart devices, the applications of ubiquitous computing are limitless for future intelligent systems. Situation inference is a non-trivial issue in the domain of ubiquitous computing research due to the challenges of mobile sensing in unrestricted environments. There are various advantages to having robust and intelligent situation inference from data streamed by mobile sensors. For instance, we would be able to gain a deeper understanding of human behaviours in certain situations via a mobile sensing paradigm. It can then be used to recommend resources or actions for enhanced cognitive augmentation, such as improved productivity and better human decision making. Sensor data can be streamed continuously from heterogeneous sources with different frequencies in a pervasive sensing environment (e.g., smart home). It is difficult and time-consuming to build a model that is capable of recognising multiple activities. These activities can be performed simultaneously with different granularities. We investigate the separability aspect of multiple activities in time-series data and develop OPTWIN as a technique to determine the optimal time window size to be used in a segmentation process. As a result, this novel technique reduces need for sensitivity analysis, which is an inherently time consuming task. To achieve an effective outcome, OPTWIN leverages multi-objective optimisation by minimising the impurity (the number of overlapped windows of human activity labels on one label space over time series data) while maximising class separability. The next issue is to effectively model and recognise multiple activities based on the user&#039;s contexts. Hence, an intelligent system should address the problem of multi-activity and context recognition prior to the situation inference process in mobile sensing applications. The performance of simultaneous recognition of human activities and contexts can be easily affected by the choices of modelling approaches to build an intelligent model. We investigate the associations of these activities and contexts at multiple levels of mobile sensing perspectives to reveal the dependency property in multi-context recognition problem. We design a Mobile Context Recognition System, which incorporates a Context-based Activity Recognition (CBAR) modelling approach to produce effective outcome from both multi-stage and multi-target inference processes to recognise human activities and their contexts simultaneously. Upon our empirical evaluation on real-world datasets, the CBAR modelling approach has significantly improved the overall accuracy of simultaneous inference on transportation mode and human activity of mobile users. The accuracy of activity and context recognition can also be influenced progressively by how reliable user annotations are. Essentially, reliable user annotation is required for activity and context recognition. These annotations are usually acquired during data capture in the world. We research the needs of reducing user burden effectively during mobile sensor data collection, through experience sampling of these annotations in-the-wild. To this end, we design CoAct-nnotate --- a technique that aims to improve the sampling of human activities and contexts by providing accurate annotation prediction and facilitates interactive user feedback acquisition for ubiquitous sensing. CoAct-nnotate incorporates a novel multi-view multi-instance learning mechanism to perform more accurate annotation prediction. It also includes a progressive learning process (i.e., model retraining based on co-training and active learning) to improve its predictive performance over time. Moving beyond context recognition of mobile users, human activities can be related to essential tasks that the users perform in daily life. Conversely, the boundaries between the types of tasks are inherently difficult to establish, as they can be defined differently from the individuals&#039; perspectives. Consequently, we investigate the implication of contextual signals for user tasks in mobile sensing applications. To define the boundary of tasks and hence recognise them, we incorporate such situation inference process (i.e., task recognition) into the proposed Intelligent Task Recognition (ITR) framework to learn users&#039; Cyber-Physical-Social activities from their mobile sensing data. By recognising the engaged tasks accurately at a given time via mobile sensing, an intelligent system can then offer proactive supports to its user to progress and complete their tasks. Finally, for robust and effective learning of mobile sensing data from heterogeneous sources (e.g., Internet-of-Things in a mobile crowdsensing scenario), we investigate the utility of sensor data in provisioning their storage and design QDaS --- an application agnostic framework for quality-driven data summarisation. This allows an effective data summarisation by performing density-based clustering on multivariate time series data from a selected source (i.e., data provider). Thus, the source selection process is determined by the measure of data quality. Nevertheless, this framework allows intelligent systems to retain comparable predictive results by its effective learning on the compact representations of mobile sensing data, while having a higher space saving ratio. This thesis contains novel contributions in terms of the techniques that can be employed for mobile situation inference and context recognition, especially in the domain of ubiquitous computing and intelligent assistive technologies. This research implements and extends the capabilities of machine learning techniques to solve real-world problems on multi-context recognition, mobile data summarisation and situation inference from mobile sensing. We firmly believe that the contributions in this research will help the future study to move forward in building more intelligent systems and applications

Understanding and supporting mobile application usage

In recent years mobile phones have evolved significantly. While the very first cellular phones only provided functionality for conducting phone calls, smartphones nowadays provide a rich variety of functionalities. Additional hardware capabilities like new sensors (e.g.~for location) and touch screens as new input devices gave rise to new use cases for mobile phones, such as navigation support, taking pictures or making payments. Mobile phones not only evolved with regard to technology, they also became ubiquitous and pervasive in people\u27s daily lives by becoming capable of supporting them in various tasks. Eventually, the advent of mobile application stores for the distribution of mobile software enabled the end-users themselves to functionally customize their mobile phones for their personal purposes and needs. So far, little is known about how people make use of the large variety of applications that are available. Thus, little support exists for end-users to make effective and efficient use of their smartphones given the huge numbers of applications that are available. This dissertation is motivated by the evolution of mobile phones from mere communication devices to multi-functional tool sets, and the challenges that have arisen as a result. The goal of this thesis is to contribute systems that support the use of mobile applications and to ground these systems\u27 designs in an understanding of user behavior gained through empirical observations. The contribution of this dissertation is twofold: First, this work aims to understand how people make use of, organize, discover and multitask between the various functionalities that are available for their smartphones. Findings are based on observations of user behavior by conducting studies in the wild. Second, this work aims to assist people in leveraging their smartphones and the functionality that is available in a more effective and efficient way. This results in tools and improved user interfaces for end-users. Given that the number of available applications for smartphones is rapidly increasing, it is crucial to understand how people make use of such applications to support smartphone use in everyday life with better designs for smartphone user interfaces.Mobiltelefone haben sich innerhalb der letzten Jahre signifikant weiterentwickelt. Während erste Modelle lediglich Sprachtelefonie zur Verfügung stellten, ermöglichen heutige Smartphones vielseitige Dienste. Technologische Fortschritte, wie beispielsweise GPS-Lokalisierung und berührungsempfindliche Displays, haben neue Einsatzbereiche für Mobiltelefone eröffnet, wie solche als Navigationsgerät oder als Fotoapparat. Doch nicht nur in Bezug auf die Technologie haben sich Mobiltelefone weiterentwickelt, sondern auch in der Verbreitung ist die Anzahl der Geräte enorm gestiegen. Sie werden allgegenwärtig im täglichen Leben genutzt, da sie ihre Anwender bei verschiedensten Aufgaben unterstützen können. Das Aufkommen von Vetriebsplattformen für die Verbreitung mobiler Software erlaubt es dem Anwender selbstständig Modifikationen an der Funktionalität seines Geräts vorzunehmen und dieses an persönliche Zwecke und Ansprüche anzupassen. Bisher ist wenig darüber bekannt, wie sich Anwender die Vielfalt zu Verfügung stehender Applikationen zu Nutze machen. Als Folge daraus gibt es bisher nur rudimentäre Unterstützung für Anwender, die Vielfalt von Applikationen effektiv und effizient einzusetzen. Diese Dissertation ist durch den Wandel des Mobiltelefons vom reinen Kommunikationsgerät hin zum multifunktionalen Werkzeug motiviert. Das Ziel dieser Arbeit ist es, Systeme für die Unterstützung einer besseren mobilen Applikationsnutzung zu entwickeln, deren Design auf dem neuen Verständnis von Benutzerverhalten beruht, das durch empirische Studien gewonnen wird. Diese Dissertation hat einen zweiteiligen Beitrag: Zum einen werden theoretische Erkenntnisse dazu erarbeitet, wie Anwender die Applikationsvielfalt nutzen, installierte Applikationen auf ihren Geräten organisieren, neue Applikationen entdecken und zwischen diesen in der Ausführung wechseln. Die Erkenntnisse hierzu beruhen auf der empirischen Beobachtung von Nutzungsverhalten. Zum anderen hat diese Arbeit ingenieurwissenschaftliche Ziele dahingehend, die Anwender von Applikationen dabei zu unterstützen, ihre Smartphones sowie deren Funktionsvielfalt effektiver und effizienter einzusetzen. Dieser Beitrag resultiert in der Beschreibung implementierter Systeme und verbesserter Benutzerschnittstellen für Anwender. Angesichts der rapide wachsenden Zahl zur Verfügung stehender mobiler Applikationen ist es wichtig, zu verstehen wie Endanwender diese nutzen, denn nur so kann die Nutzung von Smartphones gebrauchstauglicher und einfacher gestaltet werden

Mobile Data Science: Towards Understanding Data-Driven Intelligent Mobile Applications

Due to the popularity of smart mobile phones and context-aware technology, various contextual data relevant to users' diverse activities with mobile phones is available around us. This enables the study on mobile phone data and context-awareness in computing, for the purpose of building data-driven intelligent mobile applications, not only on a single device but also in a distributed environment for the benefit of end users. Based on the availability of mobile phone data, and the usefulness of data-driven applications, in this paper, we discuss about mobile data science that involves in collecting the mobile phone data from various sources and building data-driven models using machine learning techniques, in order to make dynamic decisions intelligently in various day-to-day situations of the users. For this, we first discuss the fundamental concepts and the potentiality of mobile data science to build intelligent applications. We also highlight the key elements and explain various key modules involving in the process of mobile data science. This article is the first in the field to draw a big picture, and thinking about mobile data science, and it's potentiality in developing various data-driven intelligent mobile applications. We believe this study will help both the researchers and application developers for building smart data-driven mobile applications, to assist the end mobile phone users in their daily activities.Comment: Journal, 11 pages, Double Colum

An interactive music playlist generator that responds to user emotion and context

This paper aims to demonstrate the mechanisms of a music recommendation system, and accompanying graphical user interface (GUI), that is capable of generating a playlist of songs based upon an individual’s emotion or context. This interactive music playlist generator has been designed as part of a broader system, Intended for mobile devices, which aims to suggest music based upon ‘how the user is feeling’ and ‘what the user is doing’ by evaluating real-time physiological and contextual sensory data using machine learning technologies. For instance, heart rate and skin temperature in conjunction with ambient light, temperature and global positioning satellite (GPS) could be used to a degree to infer one’s current situation and corresponding mood. At present, this interactive music playlist generator has the ability to conceptually demonstrate how a playlist can be formed in accordance with such physiological and contextual parameters. In particular, the affective aspect of the interface is visually represented as a two-dimensional arousal-valence space based upon Russell’s circumplex model of affect (1980). Context refers to environmental, locomotion and activity concepts, and are visually represented in the interface as sliders. These affective and contextual components are discussed in more detail next in Sections 2 and 3, respectively. Section 4 will demonstrate how an affective and contextual music playlist can be formed by interacting with the GUI parameters. For a comprehensive discussion in terms of the development of this research, refer to (Griffiths et al. 2013a, 2013b, 2015). Moreover, refer to Teng et al. (2013) and Yang et al. (2008) for related work in these broader research areas