Compositional coding for collaborative filtering

National Research Foundation (NRF) Singapore under its AI Singapore Programm

Explainable Recommendation: Theory and Applications

Although personalized recommendation has been investigated for decades, the wide adoption of Latent Factor Models (LFM) has made the explainability of recommendations a critical issue to both the research community and practical application of recommender systems. For example, in many practical systems the algorithm just provides a personalized item recommendation list to the users, without persuasive personalized explanation about why such an item is recommended while another is not. Unexplainable recommendations introduce negative effects to the trustworthiness of recommender systems, and thus affect the effectiveness of recommendation engines. In this work, we investigate explainable recommendation in aspects of data explainability, model explainability, and result explainability, and the main contributions are as follows: 1. Data Explainability: We propose Localized Matrix Factorization (LMF) framework based Bordered Block Diagonal Form (BBDF) matrices, and further applied this technique for parallelized matrix factorization. 2. Model Explainability: We propose Explicit Factor Models (EFM) based on phrase-level sentiment analysis, as well as dynamic user preference modeling based on time series analysis. In this work, we extract product features and user opinions towards different features from large-scale user textual reviews based on phrase-level sentiment analysis techniques, and introduce the EFM approach for explainable model learning and recommendation. 3. Economic Explainability: We propose the Total Surplus Maximization (TSM) framework for personalized recommendation, as well as the model specification in different types of online applications. Based on basic economic concepts, we provide the definitions of utility, cost, and surplus in the application scenario of Web services, and propose the general framework of web total surplus calculation and maximization.Comment: 169 pages, in Chinese, 3 main research chapter

Object-based video representations: shape compression and object segmentation

Object-based video representations are considered to be useful for easing the process of multimedia content production and enhancing user interactivity in multimedia productions. Object-based video presents several new technical challenges, however. Firstly, as with conventional video representations, compression of the video data is a requirement. For object-based representations, it is necessary to compress the shape of each video object as it moves in time. This amounts to the compression of moving binary images. This is achieved by the use of a technique called context-based arithmetic encoding. The technique is utilised by applying it to rectangular pixel blocks and as such it is consistent with the standard tools of video compression. The blockbased application also facilitates well the exploitation of temporal redundancy in the sequence of binary shapes. For the first time, context-based arithmetic encoding is used in conjunction with motion compensation to provide inter-frame compression. The method, described in this thesis, has been thoroughly tested throughout the MPEG-4 core experiment process and due to favourable results, it has been adopted as part of the MPEG-4 video standard. The second challenge lies in the acquisition of the video objects. Under normal conditions, a video sequence is captured as a sequence of frames and there is no inherent information about what objects are in the sequence, not to mention information relating to the shape of each object. Some means for segmenting semantic objects from general video sequences is required. For this purpose, several image analysis tools may be of help and in particular, it is believed that video object tracking algorithms will be important. A new tracking algorithm is developed based on piecewise polynomial motion representations and statistical estimation tools, e.g. the expectationmaximisation method and the minimum description length principle

A Systematic and Minimalist Approach to Lower Barriers in Visual Data Exploration

With the increasing availability and impact of data in our lives, we need to make quicker, more accurate, and intricate data-driven decisions. We can see and interact with data, and identify relevant features, trends, and outliers through visual data representations. In addition, the outcomes of data analysis reflect our cognitive processes, which are strongly influenced by the design of tools. To support visual and interactive data exploration, this thesis presents a systematic and minimalist approach. First, I present the Cognitive Exploration Framework, which identifies six distinct cognitive stages and provides a high-level structure to design guidelines, and evaluation of analysis tools. Next, in order to reduce decision-making complexities in creating effective interactive data visualizations, I present a minimal, yet expressive, model for tabular data using aggregated data summaries and linked selections. I demonstrate its application to common categorical, numerical, temporal, spatial, and set data types. Based on this model, I developed Keshif as an out-of-the-box, web-based tool to bootstrap the data exploration process. Then, I applied it to 160+ datasets across many domains, aiming to serve journalists, researchers, policy makers, businesses, and those tracking personal data. Using tools with novel designs and capabilities requires learning and help-seeking for both novices and experts. To provide self-service help for visual data interfaces, I present a data-driven contextual in-situ help system, HelpIn, which contrasts with separated and static videos and manuals. Lastly, I present an evaluation on design and graphical perception for dense visualization of sorted numeric data. I contrast the non-hierarchical treemaps against two multi-column chart designs, wrapped bars and piled bars. The results support that multi-column charts are perceptually more accurate than treemaps, and the unconventional piled bars may require more training to read effectively. This thesis contributes to our understanding on how to create effective data interfaces by systematically focusing on human-facing challenges through minimalist solutions. Future work to extend the power of data analysis to a broader public should continue to evaluate and improve design approaches to address many remaining cognitive, social, educational, and technical challenges

SciCADE 95: International conference on scientific computation and differential equations

This report consists of abstracts from the conference. Topics include algorithms, computer codes, and numerical solutions for differential equations. Linear and nonlinear as well as boundary-value and initial-value problems are covered. Various applications of these problems are also included

3D-in-2D Displays for ATC.

This paper reports on the efforts and accomplishments of the 3D-in-2D Displays for ATC project at the end of Year 1. We describe the invention of 10 novel 3D/2D visualisations that were mostly implemented in the Augmented Reality ARToolkit. These prototype implementations of visualisation and interaction elements can be viewed on the accompanying video. We have identified six candidate design concepts which we will further research and develop. These designs correspond with the early feasibility studies stage of maturity as defined by the NASA Technology Readiness Level framework. We developed the Combination Display Framework from a review of the literature, and used it for analysing display designs in terms of display technique used and how they are combined. The insights we gained from this framework then guided our inventions and the human-centered innovation process we use to iteratively invent. Our designs are based on an understanding of user work practices. We also developed a simple ATC simulator that we used for rapid experimentation and evaluation of design ideas. We expect that if this project continues, the effort in Year 2 and 3 will be focus on maturing the concepts and employment in a operational laboratory settings

Rigidity Percolation in Disordered Fiber Systems: Theory and Applications

Nanocomposites, particularly carbon nanocomposites, find many applications spanning an impressive variety of industries on account of their impressive properties and versatility. However, the discrepancy between the performance of individual nanoparticles and that of nanocomposites suggests continued technological development and better theoretical understanding will provide much opportunity for further property enhancement. Study of computational renderings of disordered fiber systems has been successful in various nanocomposite modeling applications, particularly toward the characterization of electrical properties. Motivated by these successes, I develop an explanatory model for `mechanical' or `rheological percolation,' terms used by experimentalists to describe a nonlinear increase in elastic modulus/strength that occurs at particle inclusion volume fractions well above the electrical percolation threshold. Specifically, I formalize a hypothesis given by \\citet*{penu}, which states that these dramatic gains result from the formation of a `rigid CNT network.' Idealizing particle interactions as hinges, this amounts to the network property of \\emph{rigidity percolation}---the emergence of a giant component (within the inclusion contact network) that is not only connected, but furthermore the inherent contacts are patterned to constrain all internal degrees of freedom in the component. Rigidity percolation has been studied in various systems (particularly the characterization of glasses and proteins) but has never been applied to disordered systems of three-dimensional rod-like particles. With mathematically principled arguments from \\emph{rigidity matroid theory}, I develop a scalable algorithm (\\emph{Rigid Graph Compression}, or \\emph{RGC}), which can be used to detect rigidity percolation in such systems by iteratively compressing provably rigid subgraphs within the rod contact networks. Prior to approaching the 3D system, I confirm the usefulness of \\emph{RGC} by using it to accurately approximate the rigidity percolation threshold in disordered systems of 2D fibers---achieving $<1\\%$ error relative to a previous exact method. Then, I develop an implementation of \\emph{RGC} in three dimensions and determine an upper bound for the rigidity percolation threshold in disordered 3D fiber systems. More work is required to show that this approximation is sufficiently accurate---however, this work confirms that rigidity in the inclusion network is a viable explanation for the industrially useful mechanical percolation. Furthermore, I use \\emph{RGC} to quantitatively characterize the effects of interphase growth and spatial CNT clustering in a real polymer nanocomposite system of experimental interest.Doctor of Philosoph