The Abstract Latent Factor ( Lf ) Models

Write a one and a half to two (1½ - 2) page paper in which you:

In the first chapter of this book, the author explains machine learning by introducing the relation among algorithms, patterns, predictions, and data. Then, he describes the diverse ways of machine learning operation. In addition, he uses statistics to clarify the discussed ideas. For example, he shows organized data on charts, tables, and graphs to make them more understandable. The publication date is just four years ago. This chapter is relevant to the research question. The author is a professor in a department of computer engineering, and he mentions the references of the information provided at the end of this chapter. The information is educational and scientific.

In the mid 20th century, computing power began to catch up, with lab interests in AT&T and IBM. By the time the 21st century came about, AI began to make an appearance outside of the laboratory. With the development of the World Wide Web, cheap computing power, big data, and social media tracking, AI is now at the center of all human information gathering, organizing, propagation, and discovery. AI techniques are key for finding, shaping, and inventing knowledge used in human interaction. Google, Amazon, and other data suppliers are citing AI techniques as the key success factor for their business models for planning for the future.

Predictive Analytics is an important requirement for our Industry and LOB applications, and BI solutions. It is a significant market - $2.0B 2014 (IDC), in which we have a minor presence.

Some machines are capable to acquire their own knowledge by extracting patterns from raw data, a phenomenon known as machine learning (ML) (Bengio, Ian and Aaron 2016). Without question, many aspects of modern society have been deeply impacted by these machine learning systems. Furthermore, ML claims to accomplish simple results that can be effortlessly understood by humans (Michie, et al. 1994). Outputs from these systems that are used in service systems include, but are not limited to offering customers new items and narrowing down their search based on their interests; language understanding, object recognition, speech perception, and identifying and favoring significant results of online searches (Yann , Yoshua and Geoffrey 2015). It is important to emphasize that even though human intervention is necessary for background knowledge, the operational phase is expected to be without human interaction (Michie, et al. 1994). Consequently, these systems must be able to learn through time. According to Alpaydin (2004), they must be able to evolve and optimize a performance criterion in order to adapt to the environmental changes to which they are exposed over time. These systems do that through the use of past experience or example data.

This paper appeared in the published proceedings of the fifteenth annual conference on innovative applications of artificial intelligence, held in Acapulco, Mexico, August 2003. Reposted on www.ets.org with permission of the Association for the Advancement of Artificial Intelligence.

Many large companies like Google, Facebook, Amazon, Netflix are leveraging unstructured data to facilitate human decision making, automate simple tasks, and to make the world a smarter place. The term big data is used to describe these unstructured datasets that are so large and complex that traditional

Abstract— Big Data is a new term used to describe a massive volume of both structured and unstructured data that is so large that it is difficult to process using traditional database and software techniques. Big Data mining is the capability of extracting useful information from these large datasets or streams of data, that due to its volume, variability, and velocity, it was not possible before to do it. The Big Data challenge is becoming one of the most exciting opportunities for the next years. We present in this issue, a broad overview of the topic, its current status, and forecast to the future. We also introduce some articles, written by influential scientists in the field, covering the most interesting and state-of-the-art topics on Big Data mining.

Kai Zheng∗, Lin Wang†, Baohua Yang∗, Yi Sun†, Yue Zhang∗, Steve Uhlig‡ ∗IBM Research †Institute of Computing Technology, Chinese Academy of Sciences ‡Queen Mary University of London

Personalization strategies that consider implicit feedback may be classified into two main categories: document-based and concept-based. Document-based strategies consider discovering user document preferences from the clickthrough information, to find out a ranking operator that optimizes the user’s browsing and clicking preferences on the retrieved documents. Joachims [2002] initially proposed extracting user clicking preferences from the clickthrough information by assuming that a user would scan the search result list from top to bottom. the click preferences are then utilized by a ranking SVM algorithmic rule [Joachims 2002] to find out a ranker that most closely fits the user’s preferences. Tan et al. [2004]

weights between input and hidden layer are initialized randomly. ELM uses analytical approach to compute weights between hidden and output layer [2], which

Therefore, on the basis of above analysis the weights on the outer layer of unsupervised learning will be used as the output for prediction of future activity of user.

As the dimensionality of the Attribute space increases, many types of data analysis and classification also become significantly harder, and, additionally, the data becomes increasingly sparse in the space it occupies which can lead to big difficulties for both supervised and unsupervised learning.

If there is much irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase is more difficult. Data preparation and filtering steps can take considerable amount of processing time. Data pre-processing includes cleaning, normalization, transformation, feature extraction and selection, etc. The product of data pre-processing is the final training

Big data analysis is one of the most important orientations in the area of information technology because of its importance in extracting information out of the available huge digital data and because of the increasingly growing use of internet services and digital transactions of the internet. In addition, data analysis plays a vital role in developing the future generation of products and services of intelligent systems for many different purposes. The National Center for Computation Technology and Applied Math CTAM deploys most of its research groups (areas of artificial intelligence, data mining, machine learning, high performance computing, language processing, software engineering) to lead national initiatives that focus on data science