Get flash to fully experience Pearltrees
Soumen Chakrabarti Here I will post comments and additional readings organized by chapters in the book, or propose new sections and chapters. Chapter 1, Introduction General additional reading:
Best Application Paper Award Winner Behavioral targeting (BT) leverages historical user behavior to select the ads most relevant to users to display. The state-of-the-art of BT derives a linear Poisson regression model from fine-grained user behavioral data and predicts click-through rate (CTR) from user history. We designed and implemented a highly scalable and efficient solution to BT using Hadoop MapReduce framework. With our parallel algorithm and the resulting system, we can build above 450 BT-category models from the entire Yahoo's user base within one day, the scale that one can not even imagine with prior systems. Moreover, our approach has yielded 20% CTR lift over the existing production system by leveraging the well-grounded probabilistic model fitted from a much larger training dataset.
Optimizing Machine Learning Programs Machine learning is often computationally bounded which implies that the ability to write fast code becomes important if you ever want to implement a machine learning algorithm. Basic tactical optimizations are covered well elsewhere , but I haven’t seen a reasonable guide to higher level optimizations, which are the most important in my experience.
Fast Gradient Descent Nic Schaudolph has been developing a fast gradient descent algorithm called Stochastic Meta-Descent (SMD). Gradient descent is currently untrendy in the machine learning community, but there remains a large number of people using gradient descent on neural networks or other architectures from when it was trendy in the early 1990s. There are three problems with gradient descent.
Building and operating large-scale information retrieval systems used by hundreds of millions of people around the world provides a number of interesting challenges. Designing such systems requires making complex design tradeoffs in a number of dimensions, including (a) the number of user queries that must be handled per second and the response latency to these requests, (b) the number and size of various corpora that are searched, (c) the latency and frequency with which documents are updated or added to the corpora, and (d) the quality and cost of the ranking algorithms that are used for retrieval. In this talk I'll discuss the evolution of Google's hardware infrastructure and information retrieval systems and some of the design challenges that arise from ever-increasing demands in all of these dimensions. I'll also describe how we use various pieces of distributed systems infrastructure when building these retrieval systems.
A graphical representation of an example Boltzmann machine. Each undirected edge represents dependency. In this example there are 3 hidden units and 4 visible units.
Pearl Pu , Derek G. Bridge , Bamshad Mobasher , Francesco Ricci (Eds.): Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys 2008, Lausanne, Switzerland, October 23-25, 2008. ACM 2008, ISBN 978-1-60558-093-7 Andrei Z.
Recommender systems or recommendation systems (sometimes replacing "system" with a synonym such as platform or engine) are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that user would give to an item (such as music , books , or movies ) or social element (e.g. people or groups ) they had not yet considered, using a model built from the characteristics of an item (content-based approaches) or the user's social environment (collaborative filtering approaches). [ 1 ] [ 2 ] Recommender systems have become extremely common in recent years. A few examples of such systems: When viewing a product on Amazon.com , the store will recommend additional items based on a matrix of what other shoppers bought along with the currently selected item. [ 3 ] Pandora Radio takes an initial input of a song or musician and plays music with similar characteristics (based on a series of keywords attributed to the inputted artist or piece of music).
Machine learning approaches to natural language processing problems such as information retrieval, document classification, and information extraction have developed rapidly over recent years. Even more recently, the joint analysis of text and images has become a significant focus for machine learning. This autumn school will summarize the state of the art in machine learning for text analysis and for joint text/image analysis, as presented by researchers active in these fields. It is intended for students who already have a familiarity with machine learning, and is designed for software developers, graduate students, and advanced researchers with an interest in learning more about this area.
Soumen Chakrabarti Here I will post comments and additional readings organized by chapters in the book, or propose new sections and chapters. Chapter 1, Introduction
Under Fisher's method, two small p-values P 1 and P 2 combine to form a smaller p-value. The yellow-green boundary defines the region where the meta-analysis p-value is below 0.05. For example, if both p-values are around 0.10, or if one is around 0.04 and one is around 0.25, the meta-analysis p-value is around 0.05.
Advertisment: In 2006 I joined Google. We are growing a Google Pittsburgh office on CMU's campus. We are hiring creative computer scientists who love programming, and Machine Learning is one the focus areas of the office. We're also currently accepting resumes for Fall 2008 intenships. If you might be interested, feel welcome to send me email: firstname.lastname@example.org .