Designing Human-Centered Collective Intelligence

Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence

Do your friends make you buy this brand?: Modeling social recommendation with topics and brands

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain

Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairly lightweight ways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design

A framework for understanding and predicting the take up and use of social networking tools in a collaborative envionment

Online collaborative environments, such as social networking environments, enable users to work together to create, modify, and share media collaboratively. However, as users can be autonomous in their actions the ability to create and form a shared understanding of the people, purpose, and process of the collaborative effort can be complex. This complexity is compounded by the natural implicit social and collaborative structure of these environments, a structure that can be modified by users dynamically and asynchronously. Some have tried to make this implicitness explicit through data mining, and allocation of user roles. However such methods can fail to adapt to the changing nature of an environment's structure relating to habits of users and their social connectedness. As a result, existing methods generally provide only a snapshot of the environment at a point in time. In addition, existing methods focus on whole user bases and the underlying social context of the environment. This makes them unsuitable for situations where the context of collaboration can change rapidly, for example the tools and widgets available for collaborative action and the users available for collaborative interactions. There is a pre-existing model for understanding the dynamic structure of these environments called the “Group Socialisation Model". This model has been used to understand how social group roles form and change over time as they go through a life cycle. This model also contains a concept of characteristic behaviours or descriptors of behaviour that an individual can use to make judgement about another individual and to create an understanding of a role or social norm that may or may not be explicit. Although studies have used components of this model to provide a means of role identification or role composition within online collaborative environments, they have not managed to provide a higher level method or framework that can replicate the entire life cycle continuously over time within these environments. Using the constructive research methodology this thesis presents a research construct in the form of a framework for replicating the social group role life cycle within online collaborative environments. The framework uses an artificial neural network with a unique capability of taking snapshots of its network structure. In conjunction with fuzzy logic inference, collaborative role signatures composed of characteristic behaviours can then be determined. In this work, three characteristic behaviours were identified from the literature for characterisation of stereotypical online behaviour to be used within a role signature: these were publisher, annotator, and lurker. The use of the framework was demonstrated on three case studies. Two of the case studies were custom built mobile applications specifically for this study, and one was the Walk 2.0 website from a National Health and Medical Research Council project. All three case studies allowed for collaborative actions where users could interact with each other to create an dynamic and diverse environment. For the use of these case studies, ethics was approved by the Western Sydney University Human Research Ethic Committee and consistent strategies for recruitment were carried out. The framework was thereby demonstrated to be capable of successfully determining role signatures composed of the above characteristic behaviours, for a range of contexts and individual users. Also, comparison of participant usage of case studies was carried out and it was established that the role signatures determined by the framework matched usage. In addition, the top contributors within the case studies were analysed to demonstrate the framework's capability of handling the dynamic and continual changing structure of an online collaborative environment. The major contribution of this thesis is a framework construct developed to propose and demonstrate a new framework approach to successfully automate and carry out the social group role model life cycle within online collaborative environments. This is a significant component of foundational work towards providing designers of online collaborative environments with the capacity of understanding the various implicit roles and their characteristic behaviours for individual users. Such a capability could enable more specific individual personalisation or resource allocation, which could in turn improve the suitability of environments developed for collaboration online

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

Challenging Social Media Threats using Collective Well-being Aware Recommendation Algorithms and an Educational Virtual Companion

Social media (SM) have become an integral part of our lives, expanding our inter-linking capabilities to new levels. There is plenty to be said about their positive effects. On the other hand however, some serious negative implications of SM have repeatedly been highlighted in recent years, pointing at various SM threats for society, and its teenagers in particular: from common issues (e.g. digital addiction and polarization) and manipulative influences of algorithms to teenager-specific issues (e.g. body stereotyping). The full impact of current SM platform design -- both at an individual and societal level -- asks for a comprehensive evaluation and conceptual improvement. We extend measures of Collective Well-Being (CWB) to SM communities. As users' relationships and interactions are a central component of CWB, education is crucial to improve CWB. We thus propose a framework based on an adaptive "social media virtual companion" for educating and supporting the entire students' community to interact with SM. The virtual companion will be powered by a Recommender System (CWB-RS) that will optimize a CWB metric instead of engagement or platform profit, which currently largely drives recommender systems thereby disregarding any societal collateral effect. CWB-RS will optimize CWB both in the short term, by balancing the level of SM threat the students are exposed to, as well as in the long term, by adopting an Intelligent Tutor System role and enabling adaptive and personalized sequencing of playful learning activities. This framework offers an initial step on understanding how to design SM systems and embedded educational interventions that favor a more healthy and positive society

Social search in collaborative tagging networks : the role of ties

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Exploiting Social Networks for Recommendation in Online Image Sharing Systems

This thesis aims to demonstrate the distinct and so far little explored value of knowledge derived from social interaction data within large web-scale image sharing systems like Flickr, Picasa Web, Facebook and others for image recommendation. I have shown how such systems can be significantly improved through personalisation that takes into account the social context of users by modelling their interactions by mining data, building and evaluating systems that incorporate this information. These improvements allow users to search and browse large online image collections more quickly and to find results that more accurately match their personal information needs when compared to existing methods. Traditional information retrieval and recommendation datasets are contrived to provide stable baselines for researchers to compare against but they rarely accurately reflect the media systems users tend to encounter online. The online photo sharing site Flickr provides rich and varied data that can be used by researchers to analyse and understand users’ interactions with images and with each other. I analyse such data by modelling the connections between users as multigraphs and exploiting the resultant topologies to produce features that can be used to train recommender systems based on machine learnt classifiers. The core contributions of this work include insight into the nature of very large-scale on- line photo collections and the communities that form around them, as well as the dynamic nature of the interactions users have with their media. I do this through the rigorous evaluation of both a probabilistic tag recommendation system and a machine learnt classifier trained to mimic user decisions regarding image preference. These implementations focus on treating the user as both a unique individual and as a member of potentially many explicit and implicit communities. I also explore the validity of the Flickr ‘Favourite’ feedback label as proxy for user preference, which is particularly important when considering other analogous media systems to which my findings transfer. My conclusions highlight how vital both social context information and the understanding of user behaviour are for online image sharing systems. In the field of information retrieval the diverse nature of users is often forgotten in the hunt for increases in esoteric performance metrics. This thesis places them back at the centre of the problem of multimedia information retrieval and shows how their variety and uniqueness are valuable traits that can be exploited to augment and improve the experience of browsing and searching shared online image collections

Learning Representations of Social Media Users

User representations are routinely used in recommendation systems by platform developers, targeted advertisements by marketers, and by public policy researchers to gauge public opinion across demographic groups. Computer scientists consider the problem of inferring user representations more abstractly; how does one extract a stable user representation - effective for many downstream tasks - from a medium as noisy and complicated as social media? The quality of a user representation is ultimately task-dependent (e.g. does it improve classifier performance, make more accurate recommendations in a recommendation system) but there are proxies that are less sensitive to the specific task. Is the representation predictive of latent properties such as a person's demographic features, socioeconomic class, or mental health state? Is it predictive of the user's future behavior? In this thesis, we begin by showing how user representations can be learned from multiple types of user behavior on social media. We apply several extensions of generalized canonical correlation analysis to learn these representations and evaluate them at three tasks: predicting future hashtag mentions, friending behavior, and demographic features. We then show how user features can be employed as distant supervision to improve topic model fit. Finally, we show how user features can be integrated into and improve existing classifiers in the multitask learning framework. We treat user representations - ground truth gender and mental health features - as auxiliary tasks to improve mental health state prediction. We also use distributed user representations learned in the first chapter to improve tweet-level stance classifiers, showing that distant user information can inform classification tasks at the granularity of a single message.Comment: PhD thesi