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Summary : 64 abstracts with links to the full papers and reader comments. Qualitative Analysis of User-based and Item-based Prediction Algorithms for Recommendation Agents ( 2005 ), by Manos Papagelis, Dimitris Plexousakis (University of Crete & FORTH-ICS, Greece) Recommendation agents employ prediction algorithms to provide users with items that match their interests.
Collaborative Filtering Research Papers
The purpose of this page is to collect links, people, ideas, keywords, papers, slides, code and data sets on nearest neighbors in a single place. Name of the problem: nearest neighbors, k nearest neighbors (kNN, k-NN), nearset neighbor search, proximity search, similarity search, approximate nearest neighbors (ANN), range queries, maximal intersection queries, post-office problem, partial match, best match file searching, best match retrieval, sequence nearest neighbors (SNN).
The Homepage of Nearest Neighbors and Similarity Search
Large-Scale Behavioral Targeting
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.Computers pound users over the head with countless options, enormous amounts of data, and small query boxes that turn keyword searches into ten lousy results. But computers are powerful; shouldn't they be able to tell us only what we need or want to know? Anyone who has used news feeds or Twitter/Facebook knows that the torrent of updates quickly becomes saturated with noise and is impossible to manage.
Self-Improving Systems that Learn Through Human Interaction
Optimizing Machine Learning Programs " Machine Learning (Th
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. Here are some of the higher level optimizations I’ve often found useful.
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. Gradient descent does not necessarily produce easily reproduced results.
Fast Gradient Descent " Machine Learning (Theory)
Challenges in Building Large-Scale Information Retrieval Systems
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.
Boltzmann machine - Wikipedia, the free encyclopedia
A Boltzmann machine is a type of stochastic recurrent neural network invented by Geoffrey Hinton and Terry Sejnowski . Boltzmann machines can be seen as the stochastic , generative counterpart of Hopfield nets . They were one of the first examples of a neural network capable of learning internal representations, and are able to represent and (given sufficient time) solve difficult combinatoric problems.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
Conference on Recommender Systems 2008
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 a 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).
Recommender system - Wikipedia, the free encyclopedia
Publications (selected) Y Yang and S Gopal . (2011) Multilabel Classification with meta-level features in a learning-to-rank framework . Machine Learning Journal, DOI: 10.1007/s10994-011-5270-7.