Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention.

Recommender systems are important approaches for dealing with the information overload problem in the big data era, and various kinds of auxiliary information, including time and sequential information, can help improve the performance of retrieval and recommendation tasks. However, it is still a challenging problem how to fully exploit such information to achieve high-quality recommendation results and improve users' experience. In this work, we present a novel sequential recommendation model, called multivariate Hawkes process embedding with attention (MHPE-a), which combines a temporal point process with the attention mechanism to predict the items that the target user may interact with according to her/his historical records. Specifically, the proposed approach MHPE-a can model users' sequential patterns in their temporal interaction sequences accurately with a multivariate Hawkes process. Then, we perform an accurate sequential recommendation to satisfy target users' real-time requirements based on their preferences obtained with MHPE-a from their historical records. Especially, an attention mechanism is used to leverage users' long/short-term preferences adaptively to achieve an accurate sequential recommendation. Extensive experiments are conducted on two real-world datasets (lastfm and gowalla), and the results show that MHPE-a achieves better performance than state-of-the-art baselines

Interval-censored Hawkes processes

Interval-censored data solely records the aggregated counts of events during specific time intervals - such as the number of patients admitted to the hospital or the volume of vehicles passing traffic loop detectors - and not the exact occurrence time of the events. It is currently not understood how to fit the Hawkes point processes to this kind of data. Its typical loss function (the point process log-likelihood) cannot be computed without exact event times. Furthermore, it does not have the independent increments property to use the Poisson likelihood. This work builds a novel point process, a set of tools, and approximations for fitting Hawkes processes within interval-censored data scenarios. First, we define the Mean Behavior Poisson process (MBPP), a novel Poisson process with a direct parameter correspondence to the popular self-exciting Hawkes process. We fit MBPP in the interval-censored setting using an interval-censored Poisson log-likelihood (IC-LL). We use the parameter equivalence to uncover the parameters of the associated Hawkes process. Second, we introduce two novel exogenous functions to distinguish the exogenous from the endogenous events. We propose the multi-impulse exogenous function - for when the exogenous events are observed as event time - and the latent homogeneous Poisson process exogenous function - for when the exogenous events are presented as interval-censored volumes. Third, we provide several approximation methods to estimate the intensity and compensator function of MBPP when no analytical solution exists. Fourth and finally, we connect the interval-censored loss of MBPP to a broader class of Bregman divergence-based functions. Using the connection, we show that the popularity estimation algorithm Hawkes Intensity Process (HIP) is a particular case of the MBPP. We verify our models through empirical testing on synthetic data and real-world data

30th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems. The amount and complexity of information itself, the number of abstraction levels of information, and the size of databases and knowledge bases are continuously growing. Conceptual modelling is one of the sub-areas of information modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers

Linking Epidemic Models and Self-exciting Processes for Online and Offline Event Diffusions

Temporal diffusion data, which comprises time-stamped events, is ubiquitous, ranging from information diffusing in online social media platforms to infectious diseases spreading in offline communities. Pressing problems, such as predicting the popularity of online information and containing epidemics, demand temporal diffusion models for understanding, modeling, and controlling diffusion dynamics. This thesis discusses diffusions of online information and epidemics by developing and connecting self-exciting processes and epidemic models. First, we propose a novel dual mixture self-exciting process for characterizing online information diffusions related to online items, such as videos or news articles. By observing that maximum likelihood estimates are separable in a Hawkes process, the model, consisting of a Borel mixture model and a kernel mixture model, jointly learns the unfolding of a heterogeneous set of cascades. When applied to cascades of the same online items, the model directly characterizes their spread dynamics and provides interpretable quantities, such as content virality and content influence decay, as well as methods for predicting the final content popularities. On two retweet cascade datasets, we show that our models capture the differences between online items at the granularity of items, publishers, and categories. Next, we propose novel ideal strategies to explore the limits of both testing and contact tracing strategies, which have been shown effective in some epidemics (e.g., SARS) but ineffective in some others (e.g., COVID-19). We then develop a superspreading random contact network that accounts for the superspreading effect of infectious diseases, where several infected cases result in most secondary infections. In simulations, we observe gaps between ideal and standard strategies by examining extensive sets of epidemic parameters, highlighting the need to explore intelligent strategies. We also present a classification of different diseases based on how containable they are under different strategies. Then, we bridge epidemic models and self-exciting processes with a novel generalized stochastic Susceptible-Infected-Recovered (SIR) model with arbitrary recovery time distributions. We articulate the relationship between recovery time distributions, recovery hazard functions, and infection kernels of self-exciting processes. We also present methods for simulating, fitting, evaluating, and predicting the generalized process. On three large Twitter diffusion datasets, we show that the modeling performance of the infection kernels varies depending on the temporal structures of diffusions and user behavior, such as the likelihood of being bots. We further improve the prediction of popularity by combining two models identified as complementary in the goodness-of-fit tests. Last, we present evently, a tool for modeling online reshare cascades, particularly retweet cascades, using self-exciting processes. This tool fills in a gap between the practitioners of online social media analysis --- usually social, political, and communication scientists --- and the accessibility to tools capable of examining online discussions. It provides a comprehensive set of functionalities for processing raw data from Twitter public APIs, modeling the temporal dynamics of processed retweet cascades and characterizing online users with a wide range of diffusion measures. Overall, this thesis studies temporal diffusions of online information and epidemics by proposing novel epidemic models and self-exciting processes. It provides tools for predicting information popularities, characterizing online items, and classifying online item categories with state-of-the-art performances. It also contributes observations in applying testing and tracing strategies in containing epidemics. Lastly, evently facilitates temporal diffusion analysis for practitioners from various fields, such as social science and epidemiology

Machine learning and privacy preserving algorithms for spatial and temporal sensing

Sensing physical and social environments are ubiquitous in modern mobile phones, IoT devices, and infrastructure-based settings. Information engraved in such data, especially the time and location attributes have unprecedented potential to characterize individual and crowd behaviour, natural and technological processes. However, it is challenging to extract abstract knowledge from the data due to its massive size, sequential structure, asynchronous operation, noisy characteristics, privacy concerns, and real time analysis requirements. Therefore, the primary goal of this thesis is to propose theoretically grounded and practically useful algorithms to learn from location and time stamps in sensor data. The proposed methods are inspired by tools from geometry, topology, and statistics. They leverage structures in the temporal and spatial data by probabilistically modeling noise, exploring topological structures embedded, and utilizing statistical structure to protect personal information and simultaneously learn aggregate information. Proposed algorithms are geared towards streaming and distributed operation for efficiency. The usefulness of the methods is argued using mathematical analysis and empirical experiments on real and artificial datasets

Accounting for carbon in the FTSE100: Numbers, narratives and credibility

PhDThe United Kingdom Government has mandated ambitious carbon objectives, requiring an 80% reduction in emissions by 2050, and a 20% interim reduction by 2020. Their achievement will require government and large companies to work together, and for each to be assured of the other’s strategic intent. An emergent carbon accounting can provide reassurance if it produces credible information that supports the claims made by each party. This thesis investigates the extent to which carbon reduction narratives are supported or contradicted by actual carbon emissions disclosed in corporate accounting reports. It also investigates whether large corporations have delivered absolute carbon reductions in support of the government’s legally binding objectives. As a result of these and other investigations, the thesis contributes to the carbon accounting literature by critiquing the method of framing emissions employed by the Greenhouse Gas Protocol, the extent to which carbon reduction is supported by meaningful managerial incentives and the means by which analysts might rebalance financial return with carbon risk in portfolio construction. Following a middle ground approach, the research employs a numbers and narratives analysis in which critical alternative narratives are created at national, sectoral and firm levels. The analysis disaggregates macro carbon emissions data, and considers carbon emissions at a corporate meso and micro level. Narratives constituted out of these numbers, together with counter-narratives generated from corporate disclosures, are then evaluated to assess their credibility. The thesis adopts a practical approach, utilising multiple framing devices. In addition to reporting scopes 1, 2 and 3 carbon emissions, it describes a business model framework in which firms are expected to disclose their carbon-material stakeholder relations. Further recommendations are aimed at aligning the interests of corporate managers, investors and financial analysts with government carbon policy in order to modify behaviour and reduce emissions trajectories towards a lower carbon future

Malaysian bilateral trade relations and economic growth

This paper examines the structure and trends of Malaysian bilateral exports and imports and then investigates whether these bilateral exports and imports have caused Malaysian economic growth. Although the structure of Malaysia’s trade has changed quite significantly over the last three decades, the direction of Malaysia’s trade remains generally the same. Broadly, ASEAN, the EU, East Asia, the US and Japan continue to be the Malaysia’s major trading partners. The Granger causality tests have shown that it is the bilateral imports that have caused economic growth in Malaysia rather than the bilateral exports

Exchange rate misalignments in ASEAN-5 countries

The purpose of this paper is to estimate the exchange rate misalignments for Indonesia, Malaysia, Philippines, Singapore and Thailand before the currency crisis. By employing the sticky-price monetary exchange rate model in the environment of vector error-correction, the results indicate that the Indonesia rupiah, Malaysian ringgit, Philippines peso and Singapore dollar were overvalued before the currency crisis while Thai baht was undervalued on the eve of the crisis. However, they suffered modest misalignment. Therefore, little evidence of exchange misalignment is found to exist in 1997:2. In particular, Indonesia rupiah, Malaysia ringgit, Philippines peso and Singapore dollar were only overvalued about 1 to 4 percent against US dollar while the Thai baht was only 2 percent undervalued against US dollar