FATREC Workshop on Responsible Recommendation Proceedings

We sought with this workshop, to foster a discussion of various topics that fall under the general umbrella of responsible recommendation: ethical considerations in recommendation, bias and discrimination in recommender systems, transparency and accountability, social impact of recommenders, user privacy, and other related concerns. Our goal was to encourage the community to think about how we build and study recommender systems in a socially-responsible manner. Recommendation systems are increasingly impacting people\u27s decisions in different walks of life including commerce, employment, dating, health, education and governance. As the impact and scope of recommendations increase, developing systems that tackle issues of fairness, transparency and accountability becomes important. This workshop was held in the spirit of FATML (Fairness, Accountability, and Transparency in Machine Learning), DAT (Data and Algorithmic Transparency), and similar workshops in related communities. With Responsible Recommendation , we brought that conversation to RecSys

A Taxonomy of Sequential Patterns Based Recommendation Systems

With remarkable expansion of information through the internet, users prefer to receive the exact information they need through some suggestions to save their time and money. Thus, recommendation systems have become the heart of business strategies of E-commerce as they can increase sales and revenue as well as customer loyalty. Recommendation systems techniques provide suggestions for items/products to be purchased, rented or used by a user. The most common type of recommendation system technique is Collaborative Filtering (CF), which takes user’s interest in an item (explicit rating) as input in a matrix known as the user-item rating matrix, and produces an output for unknown ratings of users for items from which top N recommended items for target users are defined. E-commerce recommendation systems usually deal with massive customer sequential databases such as historical purchase or click sequences. The time stamp of a click or purchase event is an important attribute of each dataset as the time interval between item purchases may be useful to learn the next items for purchase by users. Sequential Pattern Mining mines frequent or high utility sequential patterns from a sequential database. Recommendation systems accuracy will be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases into the user-item rating matrix input. Thus, integrating collaborative filtering (CF) and sequential pattern mining (SPM) of historical clicks and purchase data can improve recommendation accuracy, diversity and quality and this survey focuses on review of existing recommendation systems that are sequential pattern based exposing their methodologies, achievements, limitations, and potentials for solving more problems in this domain. This thesis provides a comprehensive and comparative study of the existing Sequential Pattern-based E-commerce recommendation systems (SP-based E-commerce RS) such as ChoRec05, ChenRec09, HuangRec09, LiuRec09, ChoiRec12, Hybrid Model RecSys16, Product RecSys16, SainiRec17, HPCRec18 and HSPCRec19. Thesis shows that integrating sequential patterns mining (SPM) of historical purchase and/or click sequences into user-item matrix for collaborative filtering (CF) (i) Improved recommendation accuracy (ii) Reduced limiting user-item rating data Sparsity (iii) Increased Novelty Rate of the recommendations and (iv) Improved Scalability of the recommendation system. Thus, the importance of sequential patterns of customer behavior in improving the quality of recommendation systems for the application domain of E-commerce is accentuated through this survey by having a comparative performance analysis of the surveyed systems

Improving Users' Acceptance in Recommender System

Ph.DDOCTOR OF PHILOSOPH

Understanding wellbeing and psychopathology in sexual minority adolescents in the UK: A multi-methods investigation

Adolescent mental health has declined in recent decades and will likely be associated with poor adult mental health and related health comorbidities in future. Within the UK a tumultuous political and economic climate is seeing widening disparities between the minority and majority groups. One form of minoritized status that merits attention during adolescence and beyond, relates to sexuality. Research consistently shows that sexual minorities experience significantly worse mental health outcomes, with adolescence being a key point of vulnerability. However, research conducted with sexual minority adolescent populations has been limited in the UK. The aim of this PhD was to investigate the prevalence of adversities in sexual minority adolescents, to understand their experiences of mental-ill health and of wellbeing, as well as the social circumstances contributing to such outcomes. This body of work aimed to add to the existing theoretical literature and to provide focus for future interventions. To do so this PhD utilises a range of methodological approaches from literature synthesis, population-based analyses, experimental psychology approaches to critical qualitative inquiry. This PhD consists of six chapters and four studies. Chapter 1 summarizes the extant literature, the political and social context in the UK and the methodological approaches adopted within this PhD. Chapter 2 identifies factors associated with subjective wellbeing in sexual minority adolescents utilising a systematic review methodology. A model of minority wellbeing was proposed, whereby factors associated with higher levels of wellbeing tended to have an external locus e.g., family/social support; whilst those factors associated with lower levels of wellbeing tended to have a more internals locus e.g., internalised homonegativity. In the absence of existing estimates, Chapter 3 uses data from The Millennium Cohort Study to provide contemporary population-based estimates of mental health, adversity, and health problems in sexual minority adolescents growing up today. Sexual minorities were more likely to experience greater mental ill-health, worse interpersonal difficulties, and poorer health related outcomes than their heterosexual counterparts. These adversities also cumulated at higher levels for sexual minorities. Chapter 4 tested the postulations of an existing sexual minority mental ill-health theoretical model (the Psychological Mediation Framework). Using an experimental approach, associations between sexual minority status, emotional dysregulation, minority specific mechanisms (i.e., internalised homonegativity), depression and wellbeing were tested via an Implicit Association Test (IAT). Support for the Psychological Mediation Framework was mixed, where conscious internalised homonegativity was linked to depression but not when it was subconscious. The relationship between minority specific mediators, depression and wellbeing varied based on whether internalised homonegativity was conscious or not and in some cases showed counterintuitive relationships (unconscious internalised homonegativity linked to higher levels of wellbeing). To contextualise and further understand these findings, developing a new theoretical framework that would map the pathways associated with mental health outcomes in sexual minority adolescents in the UK was explored. Chapter 5 employed a constructivist grounded theory methodology. Sexual minorities across the UK were interviewed about their sexual identity navigation. Findings led to the development of the Dynamic Identity Formation of Sexual minority adolescent’s theory (DIFS). Sexual identity navigation was dynamic, seeing a movement between cultures such as heternormativity and gender binarism and queerness, the enactment of these cultures, to the experience of the individual. The culture of queerness ran parallel to heternormativity and was usually accessed later in one’s developmental journey. As pernicious as the enactment of heternormativity and gender binarism could be, so could the culture of queerness – in both cultural spaces young people experienced othering. Chapter 6 summarises the contribution this PhD has for the research field, the strength of this work and future directions. Overall, it appears that sexual minorities experience significant disparities in their mental health in the UK today. Subtle messaging and social processes such as othering are having more detrimental impacts than are currently realised and can have a significant impact on an individual in the absence of discrete victimisation events. Younger sexual minorities seem particularly vulnerable as they navigate their minoritised identity. All empirical chapters have been or are pending submission to peer reviewed journal and variation in the structure of chapters reflects the recommendations of each journal

Integration of unidirectional technologies into wireless back-haul architecture

This thesis was submitted for the degree of Docter of Philosophy and awarded by Brunel University.Back-haul infrastructures of today's wireless operators must support the triple-play services demanded by the market or regulatory bodies. To cope with increasing capacity demand, the EU FP7 project CARMEN has developed a cost-effective heterogeneous multi-radio wireless back-haul architecture, which may also leverage the native multicast capabilities of broadcast technologies such as DVB-T to off-load high-bandwidth broadcast content delivery. However, the integration of such unidirectional technologies into a packet-switched architecture requires careful considerations. The contribution of this thesis is the investigation, design and evaluation of protocols and mechanisms facilitating the integration of such unidirectional technologies into the wireless back-haul architecture so that they can be configured and utilized by the spectrum and capacity optimization modules. This integration mainly concerns the control plane and, in particular, the aspects related to resource and capability descriptions, neighborhood, link and Multi Protocol Label Switching (MPLS) Label-Switched Path (LSP) monitoring, unicast and multicast LSP signalling as well as topology forming and maintenance. During the course of this study we have analyzed the problem space, proposed solutions to the resulting research questions and evaluated our approach. Our results show that the now Unidirectional Technology (UDT)-aware architecture can readily consider Unidirectional Technologies (UDTs) to distribute, for example, broadcast content

Supporting Source Code Search with Context-Aware and Semantics-Driven Query Reformulation

Software bugs and failures cost trillions of dollars every year, and could even lead to deadly accidents (e.g., Therac-25 accident). During maintenance, software developers fix numerous bugs and implement hundreds of new features by making necessary changes to the existing software code. Once an issue report (e.g., bug report, change request) is assigned to a developer, she chooses a few important keywords from the report as a search query, and then attempts to find out the exact locations in the software code that need to be either repaired or enhanced. As a part of this maintenance, developers also often select ad hoc queries on the fly, and attempt to locate the reusable code from the Internet that could assist them either in bug fixing or in feature implementation. Unfortunately, even the experienced developers often fail to construct the right search queries. Even if the developers come up with a few ad hoc queries, most of them require frequent modifications which cost significant development time and efforts. Thus, construction of an appropriate query for localizing the software bugs, programming concepts or even the reusable code is a major challenge. In this thesis, we overcome this query construction challenge with six studies, and develop a novel, effective code search solution (BugDoctor) that assists the developers in localizing the software code of interest (e.g., bugs, concepts and reusable code) during software maintenance. In particular, we reformulate a given search query (1) by designing novel keyword selection algorithms (e.g., CodeRank) that outperform the traditional alternatives (e.g., TF-IDF), (2) by leveraging the bug report quality paradigm and source document structures which were previously overlooked and (3) by exploiting the crowd knowledge and word semantics derived from Stack Overflow Q&A site, which were previously untapped. Our experiment using 5000+ search queries (bug reports, change requests, and ad hoc queries) suggests that our proposed approach can improve the given queries significantly through automated query reformulations. Comparison with 10+ existing studies on bug localization, concept location and Internet-scale code search suggests that our approach can outperform the state-of-the-art approaches with a significant margin

Building Up Recommender Systems By Deep Learning For Cognitive Services

Cognitive services provide artificial intelligence (AI) technology for application developers, who are not required to be experts on machine learning. Cognitive services are presented as an integrated service platform where end users bring abilities such as seeing, hearing, speaking, searching, user profiling, etc. to their own applications under development via simple API calls. As one of the above abilities, recommender systems serve as an indispensable building brick, especially when it comes to the information retrieval functionality in the cognitive service platform. This thesis focuses on the novel recommendation algorithms that are able to improve on recommendation quality measured by accuracy metrics, e.g., precision and recall, with advanced deep learning techniques. Recent deep learning-based recommendation models have been proved to have state-ofthe-art recommendation quality in a host of recommendation scenarios, such as rating prediction tasks, top-N ranking tasks, sequential recommendation, etc. Many of them only leverage the existing information acquired from users’ past behaviours to model them and make one or a set of predictions on the users’ next choice. Such information is normally sparse so that an accurate user behaviour model is often difficult to obtain even with deep learning. To overcome this issue, we invent various adversarial techniques and apply them to deep learning recommendation models in different scenarios. Some of these techniques involve generative models to address data sparsity and some improve user behaviour modelling by introducing an adversarial opponent in model training. We empirically show the effectiveness of our novel techniques and the enhancement achieved over existing models via thorough experiments and ablation studies on widely adopted recommendation datasets. The contributions in this thesis are as follows: 1. Propose the adversarial collaborative auto-encoder model for top-N recommendation; 2. Propose a novel deep domain adaptation cross-domain recommendation model for rating prediction tasks via transfer learning; 3. Propose a novel adversarial noise layer for convolutional neural networks and a convolutional generative adversarial model for top-N recommendation