Spatio-temporal patterns of human mobility from geo-social networks for urban computing: Analysis, models & applications

The availability of rich information about fine-grained user mobility in urban environments from increasingly geographically-aware social networking services and the rapid development of machine learning applications greatly facilitate the investigation of urban issues. In this setting, urban computing emerges intending to tackle a variety of challenges faced by cities nowadays and to offer promising approaches to improving our living environment. Leveraging massive amounts of data from geo-social networks with unprecedented richness, we show how to devise novel algorithmic techniques to reveal underlying urban mobility patterns for better policy-making and more efficient mobile applications in this dissertation. Building upon the foundation of existing research efforts in urban computing field and basic machine learning techniques, in this dissertation, we propose a general framework of urban computing with geo-social network data and develop novel algorithms tailored for three urban computing tasks. We begin by exploring how the transition data recording human movements between urban venues from geo-social networks can be aggregated and utilised to detect spatio-temporal changes of local graphs in urban areas. We further explore how this can be used as a proxy to track and predict socio-economic deprivation changes as government financial effort is put in developing areas by supervised machine learning methods. We then study how to extract latent patterns from collective user-venue interactions with the help of a spatio-temporal aware topic modeling approach for the benefit of urban infrastructure planning. After that, we propose a model to detect the gap between user-side demand and venue-side supply levels for certain types of services in urban environments to suggest further policymaking and investment optimisation. Finally, we address a mobility prediction task, the application aim of which is to recommend new places to explore in the city for mobile users. To this end, we develop a deep learning framework that integrates memory network and topic modeling techniques. Extensive experiments indicate that the proposed architecture can enhance the prediction performance in various recommendation scenarios with high interpretability. All in all, the insights drawn and the techniques developed in this dissertation make a substantial step in addressing issues in cities and open the door to future possibilities in the promising urban computing area

Pareto-based Multi-Objective Recommender System with Forgetting Curve

Recommender systems with cascading architecture play an increasingly significant role in online recommendation platforms, where the approach to dealing with negative feedback is a vital issue. For instance, in short video platforms, users tend to quickly slip away from candidates that they feel aversive, and recommender systems are expected to receive these explicit negative feedbacks and make adjustments to avoid these recommendations. Considering recency effect in memories, we propose a forgetting model based on Ebbinghaus Forgetting Curve to cope with negative feedback. In addition, we introduce a Pareto optimization solver to guarantee a better trade-off between recency and model performance. In conclusion, we propose Pareto-based Multi-Objective Recommender System with forgetting curve (PMORS), which can be applied to any multi-objective recommendation and show sufficiently superiority when facing explicit negative feedback. We have conducted evaluations of PMORS and achieved favorable outcomes in short-video scenarios on both public dataset and industrial dataset. After being deployed on an online short video platform named WeChat Channels in May, 2023, PMORS has not only demonstrated promising results for both consistency and recency but also achieved an improvement of up to +1.45% GMV

Enhancing Western E-commerce Expansion in China: Navigating Technical and Cultural Challenges through Effective Web Design

This master's dissertation explores the strategic use of website design to overcome challenges associated with Western e-commerce expansion into China. It investigates two primary dimensions: technical and cultural adaptation. Many Western e-commerce platforms have struggled in China due to inadequate adaptations to local market expectations. The research aims to provide valuable insights and practical guidance for Western e-commerce businesses aspiring to succeed in the Chinese market. It focuses on website design because of the crucial role it plays as a main tool for E-commerce companies to successfully reach its clients. At the same time, the research bridges a gap in the existing literature, which have predominantly focused on macro-level market strategies and regulatory considerations. The central research question driving this investigation is: to what extent does the alignment of website design with local cultural and technical contexts affect the success of Western e-commerce businesses in China? Employing a qualitative method of content analysis, the study evaluates 24 Western E-commerce companies based on a complementary cultural framework drawn from Hofstede’s (1980) and Schwartz’s (1992). To see the impact of the prominent depiction of local cultural and technical dimensions on the website performance metrics (traffic, pages viewed per visit, visit duration and bounce rate) a composite variable called ‘‘adaptation score’’ was calculated for each 24 Western E-commerce’s American websites and their overseas Chinese counterpart. Statistical analysis using IBM SPSS software reveals significant differences in the portrayal of Hierarchy and Harmony cultural dimensions through independent samples t-tests. Moreover, MANOVA confirms a significant association between adaptation levels and E-commerce website traffic and pages viewed per visit, offering empirical support for the significance of aligning the website design with the cultural and technical context of the target country

The Transformation of Trust in China’s Alternative Food Networks: Disruption, Reconstruction, and Development

Food safety issues in China have received much scholarly attention, yet few studies systematically examined this matter through the lens of trust. More importantly, little is known about the transformation of different types of trust in the dynamic process of food production, provision, and consumption. We consider trust as an evolving interdependent relationship between different actors. We used the Beijing County Fair, a prominent ecological farmers’ market in China, as an example to examine the transformation of trust in China’s alternative food networks. We argue that although there has been a disruption of institutional trust among the general public since 2008 when the melamine-tainted milk scandal broke out, reconstruction of individual trust and development of organizational trust have been observed, along with the emergence and increasing popularity of alternative food networks. Based on more than six months of fieldwork on the emerging ecological agriculture sector in 13 provinces across China as well as monitoring of online discussions and posts, we analyze how various social factors—including but not limited to direct and indirect reciprocity, information, endogenous institutions, and altruism—have simultaneously contributed to the transformation of trust in China’s alternative food networks. The findings not only complement current social theories of trust, but also highlight an important yet understudied phenomenon whereby informal social mechanisms have been partially substituting for formal institutions and gradually have been building trust against the backdrop of the food safety crisis in China

Contextual Affordances of Social Media, Clinical Prosess Changes and Health Service Outcomes

Never had consumers been empowered by information technologies such as social media-enabled portals that permit them to access and conduct all aspects of life and work activities through a mobile phone at any time from anywhere. WeChat, with over 963 million active monthly users, represents such a revolutionary platform. In healthcare, patients can use WeChat to make doctor appointments, access health and lab results, consult with doctors, and check on the queuing status and parking conditions in the health clinics and hospitals. Such social-media-enabled systems have transformed the relationships between consumers and businesses into a new paradigm in which the supply-side is driven by the demand-side. As a result, the new technology is fundamentally changing; not only the context in which business is conducted but also the business itself. The extant literature on technology acceptance, however, has mostly focused on technical functionalities and user characteristics without adequately considering the specific context in which the technology is used. Although these affordance concepts have advanced our knowledge about the interactions between technology and users, the specific contexts in which such interactions occur have been largely ignored. There is a critical literature gap that hinders our ability to understand and provide guidelines to help organizations deal with the complex challenges they face in managing social mediaenabled technologies in today’s changing environment. Our research attempts to bridge this critical literature gap by conceptualizing the concept of contextual affordance, and by examining its determinants and consequences in healthcare services. We use a combination of qualitative method and quantitative method. Research sites are in China across multiple healthcare facilities. The anticipated findings include validated dimensions of contextual affordance and relationships between contextual affordance and its determinants and impacts on clinical process changes and health service outcomes. Theoretically, this study extends the current understanding of affordance by considering contextual dimensions of affordance, and by examining the relationships between contextual affordance and its determinants and consequences. Practically, this study sheds new lights on how organizations should go beyond the out-of-context interactions between technologies and users by considering users’ perceived affordance of technology within the specific contexts of use

Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommender

There are roughly three stages in real industrial recommendation systems, candidates generation (retrieval), ranking and reranking. Individual-level diversity and system-level diversity are both important for industrial recommender systems. The former focus on each single user's experience, while the latter focus on the difference among users. Graph-based retrieval strategies are inevitably hijacked by heavy users and popular items, leading to the convergence of candidates for users and the lack of system-level diversity. Meanwhile, in the reranking phase, Determinantal Point Process (DPP) is deployed to increase individual-level diverisity. Heavily relying on the semantic information of items, DPP suffers from clickbait and inaccurate attributes. Besides, most studies only focus on one of the two levels of diversity, and ignore the mutual influence among different stages in real recommender systems. We argue that individual-level diversity and system-level diversity should be viewed as an integrated problem, and we provide an efficient and deployable solution for web-scale recommenders. Generally, we propose to employ the retrieval graph information in diversity-based reranking, by which to weaken the hidden similarity of items exposed to users, and consequently gain more graph explorations to improve the system-level diveristy. Besides, we argue that users' propensity for diversity changes over time in content feed recommendation. Therefore, with the explored graph, we also propose to capture the user's real-time personalized propensity to the diversity. We implement and deploy the combined system in WeChat App's Top Stories used by hundreds of millions of users. Offline simulations and online A/B tests show our solution can effectively improve both user engagement and system revenue

Disruptions as Opportunities

Disruptions as Opportunities: Governing Chinese Society with Interactive Authoritarianism addresses the long-standing puzzle of why China outlived other one-party authoritarian regimes with particular attention to how the state manages an emerging civil society. Drawing upon over 1,200 survey responses conducted in 126 villages in the Sichuan province, as well as 70 interviews conducted with Civil Society Organization (CSO) leaders and government officials, participant observation, and online research, the book proposes a new theory of interactive authoritarianism to explain how an adaptive authoritarian state manages nascent civil society. Sun argues that when new phenomena and forces are introduced into Chinese society, the Chinese state adopts a three-stage interactive approach toward societal actors: toleration, differentiation, and legalization without institutionalization. Sun looks to three disruptions—earthquakes, internet censorship, and social-media-based guerilla resistance to the ride-sharing industry—to test his theory about the three-stage interactive authoritarian approach and argues that the Chinese government evolves and consolidates its power in moments of crisis

Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation

Sequential recommendation is one of the most important tasks in recommender systems, which aims to recommend the next interacted item with historical behaviors as input. Traditional sequential recommendation always mainly considers the collected positive feedback such as click, purchase, etc. However, in short-video platforms such as TikTok, video viewing behavior may not always represent positive feedback. Specifically, the videos are played automatically, and users passively receive the recommended videos. In this new scenario, users passively express negative feedback by skipping over videos they do not like, which provides valuable information about their preferences. Different from the negative feedback studied in traditional recommender systems, this passive-negative feedback can reflect users' interests and serve as an important supervision signal in extracting users' preferences. Therefore, it is essential to carefully design and utilize it in this novel recommendation scenario. In this work, we first conduct analyses based on a large-scale real-world short-video behavior dataset and illustrate the significance of leveraging passive feedback. We then propose a novel method that deploys the sub-interest encoder, which incorporates positive feedback and passive-negative feedback as supervision signals to learn the user's current active sub-interest. Moreover, we introduce an adaptive fusion layer to integrate various sub-interests effectively. To enhance the robustness of our model, we then introduce a multi-task learning module to simultaneously optimize two kinds of feedback -- passive-negative feedback and traditional randomly-sampled negative feedback. The experiments on two large-scale datasets verify that the proposed method can significantly outperform state-of-the-art approaches. The code is released at https://github.com/tsinghua-fib-lab/RecSys2023-SINE.Comment: Accepted by RecSys'2