Exploiting the user interaction context for automatic task detection

Detecting the task a user is performing on her computer desktop is important for providing her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (i) capturing simple user interaction events on the computer desktop and (ii) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task detection performance. We also argue that good results can be achieved by training task classifiers `online' on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones

DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation

In recent years, there has been growing focus on the study of automated recommender systems. Music recommendation systems serve as a prominent domain for such works, both from an academic and a commercial perspective. A fundamental aspect of music perception is that music is experienced in temporal context and in sequence. In this work we present DJ-MC, a novel reinforcement-learning framework for music recommendation that does not recommend songs individually but rather song sequences, or playlists, based on a model of preferences for both songs and song transitions. The model is learned online and is uniquely adapted for each listener. To reduce exploration time, DJ-MC exploits user feedback to initialize a model, which it subsequently updates by reinforcement. We evaluate our framework with human participants using both real song and playlist data. Our results indicate that DJ-MC's ability to recommend sequences of songs provides a significant improvement over more straightforward approaches, which do not take transitions into account.Comment: -Updated to the most recent and completed version (to be presented at AAMAS 2015) -Updated author list. in Autonomous Agents and Multiagent Systems (AAMAS) 2015, Istanbul, Turkey, May 201

Inferring User Needs and Tasks from User Interactions

The need for search often arises from a broad range of complex information needs or tasks (such as booking travel, buying a house, etc.) which lead to lengthy search processes characterised by distinct stages and goals. While existing search systems are adept at handling simple information needs, they offer limited support for tackling complex tasks. Accurate task representations could be useful in aptly placing users in the task-subtask space and enable systems to contextually target the user, provide them better query suggestions, personalization and recommendations and help in gauging satisfaction. The major focus of this thesis is to work towards task based information retrieval systems - search systems which are adept at understanding, identifying and extracting tasks as well as supporting user’s complex search task missions. This thesis focuses on two major themes: (i) developing efficient algorithms for understanding and extracting search tasks from log user and (ii) leveraging the extracted task information to better serve the user via different applications. Based on log analysis on a tera-byte scale data from a real-world search engine, detailed analysis is provided on user interactions with search engines. On the task extraction side, two bayesian non-parametric methods are proposed to extract subtasks from a complex task and to recursively extract hierarchies of tasks and subtasks. A novel coupled matrix-tensor factorization model is proposed that represents user based on their topical interests and task behaviours. Beyond personalization, the thesis demonstrates that task information provides better context to learn from and proposes a novel neural task context embedding architecture to learn query representations. Finally, the thesis examines implicit signals of user interactions and considers the problem of predicting user’s satisfaction when engaged in complex search tasks. A unified multi-view deep sequential model is proposed to make query and task level satisfaction prediction

SMEs: ERP or virtual collaboration teams

Small firms are indeed the engines of global economic growth. Small and Medium Enterprises (SMEs) play an important role to promote economic development. SMEs in the beginning of implementing new technologies always face capital shortage and need technological assistance. Available ERP systems do not fulfil the specific requirements of Small firms. SMEs has scarce resources and manpower therefore many SMEs don?t have the possessions to buy and operate an ERP System. On the other hand competition and competitiveness of SMEs have to be strengthened. This paper briefly reviews the existing perspectives on virtual teams and their effect on SMEs management. It also discusses the main characteristics of virtual teams and clarifies the differences aspects of virtual team application in SMEs. After outlining some of the main advantages and pitfall of such teams, it concentrates on comparing of ERP and virtual collaborative teams in SMEs. Finally, it provides evidence for the need of ?Software as a Service (SaaS)? where an application is hosted as a service provided to customers across the web for SMEs as an alternative of ERP. It has been widely argued that ERP disadvantage in SMEs such as administrative expenditure and cost, isolated structure, severe lack of software flexibility, insufficient support of SMEs business and high operating cost, lead SMEs to use virtual collaborative team which is net work base solution

SMEs; Virtual research and development (R&D) teams and new product development: A literature review

Small and medium-sized enterprises (SMEs) are indeed the engines of global economic growth. Their continued growth is a major subject for the economy and employment of any country. Towards that end, virtual research and development (R&D) could be a viable option to sustain and ease the operations of SMEs. However, literature shows there has not been a great deal of research into the diverse characteristic of virtual R&D teams in SMEs. This article provides a comprehensive literature review on different aspects of virtual R&D teams collected from the reputed publications. The purpose of the literature review is to provide an outline on the structure and dynamics of R&D collaboration in SMEs. Specifying the rationale and relevance of virtual teams, the relationship between virtual R&D team for SMEs and new product development (NPD) has been examined. It concludes with identifying the gaps and feebleness in the existing literature and calls for future research in this area. It is argued to form of virtual R&D team deserves consideration at top level management for venturing into the new product development within SMEs

User context and personalized learning: a federation of contextualized attention metadata

Nowadays, personalized education is a very hot topic in technology enhanced learning (TEL) research. To support students during their learning process, the first step consists in capturing the context in which they evolve. Users typically operate in a heterogeneous environment when learning, including learning tools such as Learning Management Systems and non-learning tools and services such as e-mails, instant messaging, or web pages. Thus, user attention in a given context defines the Contextualized Attention Metadata (CAM). Various initiatives and projects allow capturing CAMs in a knowledge workers’ environment not only in the TEL area, but also in other domains like Knowledge Work Support, Personal Information Management and Information Retrieval. After reviewing main existing approaches according to some specific criteria that are of main interest for capturing and sharing user contexts, we present in this paper a framework able to gather CAMs produced by any tool or computer system. The framework is built on the Web-Based Enterprise Management (WBEM) standard dedicated to system, network and application management. Attention information specific to heterogeneous tools are represented as a unified and extensible structure, and stored into a central repository compliant with the above-mentioned standard. To facilitate access to this attention repository, we introduced a middleware layer composed of two dynamic services: the first service allows users to define the attention data they want to collect, whereas the second service is dedicated to receive and retrieve the traces produced by computer systems. An implementation for collecting and storing CAM data generated by the Ariadne Finder and Moodle validates our approach

Virtual R&D teams in small and medium enterprises: a literature review

Small and medium enterprises (SMEs) are the driving engine behind economic growth. While SMEs play a critical role in generating employment and supporting trade, they face numerous challenges, the prominent among them are the need to respond to fasting time-to-market, low-cost and rapid solutions to complex organizational problems. Towards that end, research and development (R & D) aspect deserves particular attention to promote and facilitate the operations of SMEs. Virtual R & D team could be a viable option. However, literature shows that virtual R & D teaming in SMEs is still at its infancy. This article provides a comprehensive literature review on different aspects of virtual R & D teams collected from the reputed publications. The purpose of the state-of-the-art literature review is to provide an overview on the structure and dynamics of R & D collaboration in SMEs. Specifying the foundation and importance of virtual teams, the relationship between virtual R & D team and SMEs has been examined. It concludes with the identification of the gaps in the existing literature's and calls for future research. It is argued that setting-up an infrastructure for virtual R & D team in SMEs still requires a large amount of engineering efforts and deserves consideration at top level management

Semantics-Empowered Big Data Processing with Applications

We discuss the nature of Big Data and address the role of semantics in analyzing and processing Big Data that arises in the context of Physical-Cyber-Social Systems. We organize our research around the Five Vs of Big Data, where four of the Vs are harnessed to produce the fifth V - value. To handle the challenge of Volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision-making. To handle the challenge of Variety, we resort to the use of semantic models and annotations of data so that much of the intelligent processing can be done at a level independent of heterogeneity of data formats and media. To handle the challenge of Velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize relevant new concepts, entities and facts. To handle Veracity, we explore the formalization of trust models and approaches to glean trustworthiness. The above four Vs of Big Data are harnessed by the semantics-empowered analytics to derive value for supporting practical applications transcending physical-cyber-social continuum

SMEs; Virtual research and development (R&D) teams and new product development: A literature review

Small and medium-sized enterprises (SMEs) are indeed the engines of global economic growth. Their continued growth is a major subject for the economy and employment of any country. Towards that end, virtual research and development (R&D) could be a viable option to sustain and ease the operations of SMEs. However, literature shows there has not been a great deal of research into the diverse characteristic of virtual R&D teams in SMEs. This article provides a comprehensive literature review on different aspects of virtual R&D teams collected from the reputed publications. The purpose of the literature review is to provide an outline on the structure and dynamics of R&D collaboration in SMEs. Specifying the rationale and relevance of virtual teams, the relationship between virtual R&D team for SMEs and new product development (NPD) has been examined. It concludes with identifying the gaps and feebleness in the existing literature and calls for future research in this area. It is argued to form of virtual R&D team deserves consideration at top level management for venturing into the new product development within SMEs.Virtual teams, small and medium enterprises, new product development, R&D