Collaborative filtering for mobile application recommendation with implicit feedback

This paper introduces a novel dataset regarding the installation of mobile applications in users devices, and benchmarks multiple well-established collaborative filtering techniques, leveraging on the user implicit feedback extracted from the data. Our experiments use 3 snapshots provided by Aptoide, one of the leading mobile application stores. These snapshots provide information about the installed applications for more than 4 million users in total. Such data allow us to infer the users activity over time, which corresponds to an implicit measure of interest in a certain application, as we consider that installs reflect a positive user opinion on an app, and, inversely, uninstalls reflect a negative user opinion. Since recommendation systems usually use explicit rating data, we have filtered and transformed the existing data into binary ratings. We have trained several recommendation models, using the Surprise Python scikit, comparing baseline algorithms to neighborhood-based and matrix factorization methods. Our evaluation shows that SVD-based and KNN-based methods achieve good performance scores while being computationally efficient, suggesting that they are suitable for recommendation in this novel dataset.info:eu-repo/semantics/acceptedVersio

Recommending places blased on the wisdom-of-the-crowd

The collective opinion of a great number of users, popularly known as wisdom of the crowd, has been seen as powerful tool for solving problems. As suggested by Surowiecki in his books [134], large groups of people are now considered smarter than an elite few, regardless of how brilliant at solving problems or coming to wise decisions they are. This phenomenon together with the availability of a huge amount of data on the Web has propitiated the development of solutions which employ the wisdom-of-the-crowd to solve a variety of problems in different domains, such as recommender systems [128], social networks [100] and combinatorial problems [152, 151]. The vast majority of data on the Web has been generated in the last few years by billions of users around the globe using their mobile devices and web applications, mainly on social networks. This information carries astonishing details of daily activities ranging from urban mobility and tourism behavior, to emotions and interests. The largest social network nowadays is Facebook, which in December 2015 had incredible 1.31 billion mobile active users, 4.5 billion “likes” generated daily. In addition, every 60 seconds 510 comments are posted, 293, 000 statuses are updated, and 136,000 photos are uploaded1. This flood of data has brought great opportunities to discover individual and collective preferences, and use this information to offer services to meet people’s needs, such as recommending relevant and interesting items (e.g. news, places, movies). Furthermore, it is now possible to exploit the experiences of groups of people as a collective behavior so as to augment the experience of other. This latter illustrates the important scenario where the discovery of collective behavioral patterns, the wisdom-of-the-crowd, may enrich the experience of individual users. In this light, this thesis has the objective of taking advantage of the wisdom of the crowd in order to better understand human mobility behavior so as to achieve the final purpose of supporting users (e.g. people) by providing intelligent and effective recommendations. We accomplish this objective by following three main lines of investigation as discussed below. In the first line of investigation we conduct a study of human mobility using the wisdom-of- the-crowd, culminating in the development of an analytical framework that offers a methodology to understand how the points of interest (PoIs) in a city are related to each other on the basis of the displacement of people. We experimented our methodology by using the PoI network topology to identify new classes of points of interest based on visiting patterns, spatial displacement from one PoI to another as well as popularity of the PoIs. Important relationships between PoIs are mined by discovering communities (groups) of PoIs that are closely related to each other based on user movements, where different analytical metrics are proposed to better understand such a perspective. The second line of investigation exploits the wisdom-of-the-crowd collected through user-generated content to recommend itineraries in tourist cities. To this end, we propose an unsupervised framework, called TripBuilder, that leverages large collections of Flickr photos, as the wisdom-of- the-crowd, and points of interest from Wikipedia in order to support tourists in planning their visits to the cities. We extensively experimented our framework using real data, thus demonstrating the effectiveness and efficiency of the proposal. Based on the theoretical framework, we designed and developed a platform encompassing the main features required to create personalized sightseeing tours. This platform has received significant interest within the research community, since it is recognized as crucial to understand the needs of tourists when they are planning a visit to a new city. Consequently this led to outstanding scientific results. In the third line of investigation, we exploit the wisdom-of-the-crowd to leverage recommendations of groups of people (e.g. friends) who can enjoy an item (e.g. restaurant) together. We propose GroupFinder to address the novel user-item group formation problem aimed at recommending the best group of friends for a pair. The proposal combines user-item relevance information with the user’s social network (ego network), while trying to balance the satisfaction of all the members of the group for the item with the intra-group relationships. Algorithmic solutions are proposed and experimented in the location-based recommendation domain by using four publicly available Location-Based Social Network (LBSN) datasets, showing that our solution is effective and outperforms strong baselines

Movie / Cinema: Rearrangements of the Apparatus in Contemporary Movie Circulation

This thesis investigates how cinema’s specificities are defined in relation to technological developments. I propose that the most appropriate way to do this is by taking the whole cinematographic circuit into account – that is, the complete set of socio-technical operations that are involved in the medium, as remote as they might seem to be from actual cinematographic practices. I depart from the definition of circulation as a socio-technical continuum of the production, distribution, exhibition and evaluation of movies, explaining how these activities might be enacted in three different technological regimes: film, video and digital computation. Then, following an account of the early history of the pirate film society Cine Falcatrua (2003-2005), I show how the specificity of the medium is constituted and preserved throughout its technical progress. Acknowledging the limits of traditional film and screen studies to deal with these questions, I attempt to find an alternative research approach by engaging in practice-based investigation using curatorial strategies. By bringing together and analysing different film and art pieces in an exhibition entitled Denied Distances (2009), I propose a framework that allows an understanding of how media technology are defined in relation to one another, exposing how seemingly expanded practices such as installations and performances might be contained within conventional cinematographic apparatus. I conclude by suggesting that, in order to keep up with the ever-changing nature of the medium, the study of cinema would profit from engaging the extremes of scientific criticism and art practice

Social informatics

5th International Conference, SocInfo 2013, Kyoto, Japan, November 25-27, 2013, Proceedings</p

Multi-facet graph mining with contextualized projections

The goal of my doctoral research is to develop a new generation of graph mining techniques, centered around my proposed idea of multi-facet contextualized projections, for more systematic, flexible, and scalable knowledge discovery around massive, complex, and noisy real-world context-rich networks across various domains. Traditional graph theories largely overlook network contexts, whereas state-of-the-art graph mining algorithms simply regard them as associative attributes and brutally employ machine learning models developed in individual domains (e.g., convolutional neural networks in computer vision, recurrent neural networks in natural language processing) to handle them jointly. As such, essentially different contexts (e.g., temporal, spatial, textual, visual) are mixed up in a messy, unstable, and uninterpretable way, while the correlations between graph topologies and contexts remain a mystery, which further renders the development of real-world mining systems less principled and ineffective. To overcome such barriers, my research harnesses the power of multi-facet context modeling and focuses on the principle of contextualized projections, which provides generic but subtle solutions to knowledge discovery over graphs with the mixtures of various semantic contexts

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Innovation and the economic performance of the primary information sector: a multidisciplinary approach

The aim of this research is to understand and compare the implications of recent technical changes for the development and performance of three key component sub-sectors of the primary information sector (PIS): the Information and Communication Technology supply industries; Telecommunications services and Media services. In this study, the author first reviews the most important economic theories explaining the links between technical change or progress and economic performance (i.e. Neoclassical and Neo- Schumpeterian / Evolutionary), as well as the relatively recent “New Economy” writings about the latest wave of technological innovations. Secondly, the author adopts an historical and evolutionary approach to examine the evolution of three main groups of activities representing the PIS industries in the case of the USA. The study provides an account of the main technical innovations but also the regulatory, organisational, managerial and stylistic changes that follow and complete these innovations. These changes contribute to the creation of new industries and markets and, in a fundamental way to the harvesting of their benefits. Three key groups of activities are taken as case studies for empirical and historical analyses: first, the computer industry, second, the wireline telecommunication industry, and third, the audiovisual content and distribution media services. In the case of the computer and media content industries, while providing an account of the links between innovations and economic performance, the study also examines the evolution from manufacturing-type activities into activities better described as services. In the case of the wireline telecommunication industries, the author highlights the separation of different activities into different modules and highlights the role of the regulator as current “system integrator”. The perspective adopted in this research is critical of those approaches that rely on mainstream economics to provide the main framework for explaining the effect of technical change on the economic performance of these sectors. This study, rather, emphasises the necessity of using a variety of theories to explain the evolution of these sectors. In addition to an historical and evolutionary approach, this study proposes a re-defined version of Baumol’s theory of cost disease (based on a notion of “creative inputs”). It also draws on relevant aspects of the service economics literature and modularity theories (defined as a subset of theories within the Complex Evolving Systems’ school of thought)