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Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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Modern Applied Statistics With S

Matrix Factorization Techniques for Recommender Systems

In e-commerce, online product reviews significantly influence the purchase decisions of buyers and the marketing strategies employed by vendors. However, the abundance of reviews and their uneven quality make distinguishing between useful and useless reviews difficult for potential customers, thereby diminishing the benefits of online review systems. To address this problem, we develop a semi-supervised system called Online Review Quality Mining (ORQM). Embedded with independent component analysis and semi-supervised ensemble learning, ORQM exploits two opportunities: the improvement of classification performance through the use of a few labeled instances and numerous unlabeled instances, and the effectiveness of the social characteristics of e-commerce communities as identifiers of influential reviewers who write high-quality reviews. Three complementary experiments on datasets from Amazon.com show that ORQM exhibits remarkably higher performance in classifying reviews of different quality levels than do other well-accepted state-of-the-art text mining methods. The high performance of ORQM is also consistent and stable even under limited availability of labeled instances, thereby outperforming other baseline methods. The experiments also reveal that (1) the social features of reviewers are important in deriving better classification results; (2) classification results are affected by product type given the different purchase habits of consumers; and (3) reviews are contingent on the inherent nature of products, such as whether they are search goods or experience goods, and digital products or physical products, through which purchase decisions are influenced.

Capturing the essence of word-of-mouth for social commerce: Assessing the quality of online e-commerce reviews by a semi-supervised approach

An efficient and reliable approach for quality-of-service-aware service composition

Online social networking sites have become popular platforms on which users can link with each other and share information, not only basic rating information but also information such as contexts, social relationships, and item contents. However, as far as we know, no existing works systematically combine diverse types of information to build more accurate recommender systems. In this paper, we propose a novel context-aware hierarchical Bayesian method. First, we propose the use of spectral clustering for user-item subgrouping, so that users and items in similar contexts are grouped. We then propose a novel hierarchical Bayesian model that can make predictions for each user-item subgroup, our model incorporate not only topic modeling to mine item content but also social matrix factorization to handle ratings and social relationships. Experiments on an Epinions dataset show that our method significantly improves recommendation performance compared with six categories of state-of-the-art recommendation methods in terms of both prediction accuracy and recall. We have also conducted experiments to study the extent to which ratings, contexts, social relationships, and item contents contribute to recommendation performance in terms of prediction accuracy and recall.

/pdf/context-ware-collaborative-topic-regression-with-social-2z3u533v16.pdf

Context- ware collaborative topic regression with social matrix factorization for recommender systems

A hybrid approach for movie recommendation via tags and ratings

In order to address the data sparsity problem in recommender systems, in recent years, Cross-Domain Recommendation (CDR) leverages the relatively richer information from a source domain to improve the recommendation performance on a target domain with sparser information. However, each of the two domains may be relatively richer in certain types of information (e.g., ratings, reviews, user profiles, item details, and tags), and thus, if we can leverage such information well, it is possible to improve the recommendation performance on both domains simultaneously (i.e., dual-target CDR), rather than a single target domain only. To this end, in this paper, we propose a new framework, DTCDR, for Dual-Target Cross-Domain Recommendation. In DTCDR, we first extensively utilize rating and multi-source content information to generate rating and document embeddings of users and items. Then, based on Multi-Task Learning (MTL), we design an adaptable embedding-sharing strategy to combine and share the embeddings of common users across domains, with which DTCDR can improve the recommendation performance on both richer and sparser (i.e., dual-target) domains simultaneously. Extensive experiments conducted on real-world datasets demonstrate that DTCDR can significantly improve the recommendation accuracies on both richer and sparser domains and outperform the state-of-the-art single-domain and cross-domain approaches.

Xiaolin Zheng

Papers

Capturing the essence of word-of-mouth for social commerce: Assessing the quality of online e-commerce reviews by a semi-supervised approach

An efficient and reliable approach for quality-of-service-aware service composition

Context- ware collaborative topic regression with social matrix factorization for recommender systems

A hybrid approach for movie recommendation via tags and ratings

DTCDR: A Framework for Dual-Target Cross-Domain Recommendation