Research of Clustering Method and Its Application for Big Data

近些年来，随着计算机科学与技术的快速发展，在很多行业中产生了越来越 多的海量数据信息。聚类作为数据挖掘的一个非常受关注的分支学科，在这种情 况下得到了长足的发展，一系列经典的聚类算法被研究者提出，但目前能应用于 大数据聚类的算法不多，ApacheMahout推出的聚类算法只有5种，其中有4种 基于Kmeans算法开发的，Spark官方推出的聚类算法目前只有Kmeans。一些效 果较好的聚类算法，它们的时间复杂度比较高，开发出适应大数据聚类的难度较 大。传统的Kmeans可以用于大数据聚类，但其迭代过程涉及到多次的HDFS文 件系统的读写操作也非常费时。 本文通过引入聚类特征树，...In recent years, more and more huge amounts of data information is produced in many industries in the condition of the fast progress of computer science and technology. Cluster analysis technology is an important part of Data Mining, and of course, the development of cluster analysis is also fast and mature in this condition. a series of classical cluster algorithm is proposed, but cluster alg...学位：工学硕士院系专业：软件学院_软件工程学号：2432012115228

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

A complementary view for better understanding the term computational thinking

The term Computational Thinking is closely related to efforts connected to teach a systematic and well-structured way of problem solving that includes a set of tools and techniques used in Computer Science. While substantial research in this field has shown promising outcomes concerning distinct intervention programs and teaching initiatives, the term Computational Thinking itself requires to be revised in order to get a wider consensus about its meaning and purpose. This paper contributes to the ongoing quest concerning the definition of the term by starting with a fundamental perspective on computational theory and corresponding concepts in order to describe the theoretical building blocks of a systematic view to further elaborate on an approach for teaching and learning about Computational Thinking. Additionally, based on this foundational effort, more advanced concepts are presented and discussed in order to better understand this domain. Finally, the paper identifies and discusses a set of relevant challenges taking a cognitive psychology perspective on Computational Thinking