A Dynamic Trust Relations-Based Friend Recommendation Algorithm in Social Network Systems

A discovered algorithm based on the dynamic trust relations of users in a social network system (SNS) was proposed aiming at getting useful information more efficiently in an SNS. The proposed dynamic model combined the interests and trust relations of users to explore their good friends for recommendations. First, the network based on the interests and trust relations of users was set up. Second, the temporal factor was added to the model, then a dynamic model of the degree of the interest and trust relations of the users was calculated. Lastly, the similarities among the users were measured via this dynamic model, and the recommendation list of good friends was achieved. Results showed that the proposed algorithm effectively described the changes in the interest similarities and trust relations of users with time, and the recommended result was more accurate and effective than the traditional ones

Towards trust-aware recommendations in social networks

Recommender systems have been strongly researched within the last decade. With the emergence and popularization of social networks a new fi eld has been opened for social recommendations. Introducing new concepts such as trust and considering the network topology are some of the new strategies that recommender systems have to take into account in order to adapt their techniques to these new scenarios. In this thesis a simple model for recommendations on twitter is developed to apply some of the known techniques and explore how well the state of the art does in a real scenario. The thesis can serve as a basis for further social recommender system research

Peer effects on perseverance

Successful performance – be it in school, at the job, or in sports activities – requires perseverance, i.e., persistent work on a demanding task. We investigate in a controlled laboratory experiment how an individual’s social environment affects perseverance. We find evidence for two kinds of peer effects: being observed by a peer can serve as a commitment device, while observing a peer can be informative. In particular, we show that successful peers affect perseverance positively if they communicate their success in a motivating way and negatively otherwise, while perseverance is unaffected by unsuccessful peers. Our experimental results suggest that peers affect perseverance indirectly, via influencing self-confidence. We turn to field data from an educational setting and find that students seem to be able to harness the power of peer effects, by selecting into groups that help them reach their goals

Controllability and explainability in a hybrid social recommender system

The growth in artificial intelligence (AI) technology has advanced many human-facing applications. The recommender system is one of the promising sub-domain of AI-driven application, which aims to predict items or ratings based on user preferences. These systems were empowered by large-scale data and automated inference methods that bring useful but puzzling suggestions to the users. That is, the output is usually unpredictable and opaque, which may demonstrate user perceptions of the system that can be confusing, frustrating or even dangerous in many life-changing scenarios. Adding controllability and explainability are two promising approaches to improve human interaction with AI. However, the varying capability of AI-driven applications makes the conventional design principles are less useful. It brings tremendous opportunities as well as challenges for the user interface and interaction design, which has been discussed in the human-computer interaction (HCI) community for over two decades. The goal of this dissertation is to build a framework for AI-driven applications that enables people to interact effectively with the system as well as be able to interpret the output from the system. Specifically, this dissertation presents the exploration of how to bring controllability and explainability to a hybrid social recommender system, included several attempts in designing user-controllable and explainable interfaces that allow the users to fuse multi-dimensional relevance and request explanations of the received recommendations. The works contribute to the HCI fields by providing design implications of enhancing human-AI interaction and gaining transparency of AI-driven applications

Education and Training of Specialist Sexual Offence Investigators in Victoria, Australia from 2009 to 2011

The topic of training specifically designed for investigators of sexual offences has received little attention from academic researchers to date. Previous studies have not described training provided to police investigators of sexual offences in Australia. This thesis developed Turnley’s Framework for the Examination of Police Training in Sexual Assault Investigation, to examine and describe a Sexual Offences and Child Abuse Investigative Teams (SOCIT) Course, provided to Victorian Police from 2009 to 2011. This entailed triangulation of findings from non-participant observations of one SOCIT Course, with quantitative and qualitative data sourced though an in-depth interview with course trainers; feedback sheets voluntarily completed by trainees who undertook the course and responses from an online survey of 44 police who completed a course between 2009 and 2011. A description of the course design, resourcing, content, delivery, individual and organisational outcomes are presented as findings. Trainees reported the SOCIT course to be highly relevant for the work of specialist sexual assault investigators, with 80% of survey respondents self-reporting a change in their attitudes towards victims of sexual offences as a result of the SOCIT training. Despite these self-reports, findings from the survey indicate the maintenance of negative attitudes by some police in relation victims. The findings of this thesis concur and support findings of the Policing Just Outcomes Project with regard to the need for police to focus on, and refine the process of selection and recruitment, for this specialised area of police work