Quantitative analysis of Matthew effect and sparsity problem of recommender systems

Recommender systems have received great commercial success. Recommendation has been used widely in areas such as e-commerce, online music FM, online news portal, etc. However, several problems related to input data structure pose serious challenge to recommender system performance. Two of these problems are Matthew effect and sparsity problem. Matthew effect heavily skews recommender system output towards popular items. Data sparsity problem directly affects the coverage of recommendation result. Collaborative filtering is a simple benchmark ubiquitously adopted in the industry as the baseline for recommender system design. Understanding the underlying mechanism of collaborative filtering is crucial for further optimization. In this paper, we do a thorough quantitative analysis on Matthew effect and sparsity problem in the particular context setting of collaborative filtering. We compare the underlying mechanism of user-based and item-based collaborative filtering and give insight to industrial recommender system builders

A methodology of personalized recommendation system on mobile device for digital television viewers

With the increasing of the number of digital television (TV) channels in Thailand, this becomes a problem of information overload for TV viewers. There are mass numbers of TV programs to watch but the information about these programs is poor. Therefore, this work presents a personalized recommendation system on mobile device to recommend a TV program that matches viewer’s interests and/or needs.The main mechanism of the system is content-based similarity analysis (CBSA).Initially, the viewer defines favorite programs, and then the system utilize this list as query to find their annotations on the WWW. These annotations will be used to find other programs that are similar by using CBSA. Finally, all similar programs are grouped to the same class and stored as a dataset in a personal mobile device. For the usage, if a TV program matches the interest and specified time of viewer, the system on mobile device will notify the viewer individually

Supporting Regularized Logistic Regression Privately and Efficiently

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Increasing concerns over data privacy make it more and more difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used machine learning model in various disciplines while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluation on several studies validated the privacy guarantees, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc

The Chinese Approach To Web Journalism: A Comparative Analysis

This thesis explores the distinctive forms of journalism that have emerged in mainstream news websites in mainland China. Two case studies, the 2008 Beijing Olympic Games and the H1N1 influenza pandemic in 2009, are employed to identify features in Chinese and Western news online. Specifically, a comparison is made between the in-depth news sections of popular mainstream news websites in China and those in the United States, the United Kingdom, and New Zealand. The study finds that the Chinese version of mainstream web news genre differs significantly from the Western version. This thesis argues that journalists’ practice is strongly context dependent. Distinctive economic, organizational, social and cultural factors contribute to shaping Chinese web journalism in a way that contradicts the notion of a homogeneous worldwide journalism or of a single set of norms for journalism. The study challenges the dominance of the political explanatory framework that considers political factors as the most important approach to study Chinese web-based media. In the face of a sparse literature and sporadic studies concerning the development of the internet as a novel platform in China for news production and transmission, this thesis aims to bring more academic interest to an overlooked research area and to contribute to a broader understanding of the actual diversity of global communication research

Contextual Models for Sequential Recommendation

Recommender systems aim to capture the interests of users in order to provide them with tailored recommendations for items or services they might like. User interests are often unique and depend on many unobservable factors including internal moods or external events. This phenomenon creates a broad range of tasks for recommendation systems that are difficult to address altogether. Nevertheless, analyzing the historical activities of users sheds light on the characteristic traits of individual behaviors in order to enable qualified recommendations. In this thesis, we deal with the problem of comprehending the interests of users, searching for pertinent items, and ranking them to recommend the most relevant items to the users given different contexts and situations. We focus on recommendation problems in sequential scenarios, where a series of past events influences the future decisions of users. These events are either the developed preferences of users over a long span of time or highly influenced by the zeitgeist and common trends. We are among the first to model recommendation systems in a sequential fashion via exploiting the short-term interests of users in session-based scenarios. We leverage reinforcement learning techniques to capture underlying short- and long-term user interests in the absence of explicit feedback and develop novel contextual approaches for sequential recommendation systems. These approaches are designed to efficiently learn models for different types of recommendation tasks and are extended to continuous and multi-agent settings. All the proposed methods are empirically studied on large-scale real-world scenarios ranging from e-commerce to sport and demonstrate excellent performance in comparison to baseline approaches

Indie encounters: exploring indie music socialising in China

Indie music, a genre deeply rooted in rock and punk music, is renowned for its independence from major commercial record labels. It has emerged as a choice for music consumers seeking alternatives to mainstream popular music, catering to a niche music preference. The minority nature of indie music not only provides its lovers with a profound space for individual expression and a sense of collective belonging but also introduces other challenges into their social lives. Recently, the field of music sociology has proposed a more diverse perspective to observe and analyse the intricate role of music for individuals and society. In this context, regarding Chinese indie music lovers with niche music preferences, how their indie music practices integrate into their social lives and how they navigate their niche music tastes have become worthwhile topics of exploration. Drawing on interviews with 31 Chinese indie music lovers and extensive online ethnography, this thesis investigates how Chinese indie music lovers comprehend and engage with indie music, and how the power of indie music shapes them and their social behaviours. I employ the theoretical framework of ‘music in action’ (Hennion, 2001; DeNora, 2011, 2016) and symbolic interactionism (Mead, 1934; Goffman, 1959; Blumer, 1969) to examine the dynamic and multifaceted roles of indie music in the social lives of Chinese indie music lovers. I develop a concept of ‘music socialising’ to delve into several key aspects of music lovers’ social practices. I contend that through various forms of musical activities such as music selection, live music attendance, and digital practices, indie music lovers exhibit strategic and reflexive characteristics in their music practices. These practices actively contribute to constructing and maintaining self and identity, negotiating social ties, and forming and mediating collectivity within a broader social landscape. It is through these processes that the music practices of Chinese indie music lovers are endowed with meanings, thereby shaping their social reality. This thesis presents a rich and nuanced picture of the social experiences of Chinese indie music lovers, uncovering the transformative power of their indie music practices. It presents a compelling argument for the significance of music as a social agency, highlighting the complex interactions between music, individuals, and society. By bridging theoretical insights with rich empirical data, this thesis contributes to our understanding of the socio-cultural dimensions of music, offering fresh perspectives on the role of indie music in contemporary Chinese society

An analysis of strategic management in the digital music industry in a Chinese context

There are elements of cultural innovation only partly articulated in managing the business of Digital Music within academic research in a Chinese context. This thesis research into one question, how far management and operational systems developed with a western background can be applied efficiently to the Chinese context within the field of the Digital Music Industry? The study adopted a chronological approach. It followed the development history of three timelines, the development of management theories and logic in China and the West, the development of global Digital Music, the development of China's Digital Music Industry, which including understanding a critical introduction to management in its historical and intellectual context which provided a useful expansion of the issues raised. This research analyses China's Digital Music Industry from the perspective of the insider, with a people-oriented research angle and a comprehensive methodology based on an interpretive approach combined with dialectical thinking. The research distinguishes China's Digital Music Industry from other mature Digital Music industries and highlights the contemporary challenges it presents in the current context. This thesis begins by building a theoretical framework of Western management and its development, contrasting this with a Chinese experience of theories and philosophy of management. It tested these theories by analysing the changes and growth of Digital Music management in China from the external environment perspective and a case study of QQ Music. The research compares the similarities and differences between China's Digital Music Industry and others which include definitions of Digital Music, historical developments, people's concept of consumption, attitude, and behavioural habits around Digital Music. It reviews the literature on management research to conceptualise Western theories combined with the case study of QQ music, to make explicit how they apply or do not apply in China, and to be more specific, within the Chinese Digital Music Industry. The research defines the mission and goal of Digital Music in a Chinese context. More importantly, based on the analysis to understand the Chinese Digital Music management logic, makes clear the unique attributes (service as the core competitiveness), the development pattern of China's Digital Music Industry (an online and offline interactive digital business ecosystem) and offers a way to extend existing theories (the collision of fan economy, experience economy and the Long Tail theory). The research has collected a lot of valuable first-hand data, including many hard-to-reach groups and includes non-public data from the company and local government. The study concludes that Western management theories are distinct from China's experience in the Digital Music Industry. This lies in, particularly, the core profit model and consumer habits of Digital Music in China and their difference to the West. Consumers have different perceptions of the value of music content and service. It is valuable to seek new insights into advanced business models and management theories which is set to enhance the study of China's Digital Music Industry and which may provide the practical assessment of good practice in a Chinese context to inform management practice from non-Western models