mahout hadoop

There are many similarity implementations in the Mahout recommendation system that implement calculations that do not have a similarity between user or item. For data sources with different data volumes and data types, different similarity calculation methods are required to improve the recommended performance, and in Mahout, a large number of components for computing similarity are provided, each of which

Last time we talked about Mahout's Computational project module Mahout Math. This contains a lot of commonly used mathematical calculations or statistical aspects, there are many things that may be used, so there is a good understanding of the needs of these foundations. Mahout provides a number of tools for the command-line, listed below all the commands, of course, this will change, and each has a differe

In reality, the recommended systems are generally based on the collaborative filtering algorithm, such algorithms usually need to calculate the user and user or project and project similarity, for data and data types of different data sources, need different similarity calculation method to improve the recommended performance, The mahout provides a large number of components for computing similarity, and these components implement different methods of

algorithm during text preprocessing, and adjusting training parameters when training classifier. Repeat the process until a satisfactory model is obtained. to categorize text:Once the text classifier is created, you can enter a text and output a category. Step1: Upload the original data sport and User-sport folders needed to HDFs Sport folder:Used to train text classifiers to contain multiple subfolders, each of which is a categorized article in a real-world project where the raw data need

In reality, the recommendation system is generally based on collaborative filtering algorithms, which usually need to calculate the user and user or project and project similarity, for data volume and data types of different data sources, need different similarity calculation method to improve the recommended performance, In Mahout, a large number of components are provided for computing similarity, and these components implement different similarity

compelling vision for big data and Hadoop, and the ultimate expectation of many companies for big data platforms. as more data becomes available, the value of future big data platforms depends more on how much AI is being calculated. Now machine learning is slowly spanning the ivory tower, from a small number of academics to research the science and technology issues into many enterprises are validating the use of data analysis tools, and has become

Recommendation algorithms in Mahout include User-based Recommender, Item-based Recommender, and Slope-One Recommender. 1. User-based Recommender The main idea of this algorithm is: the product most similar to user u is probably the product that user u prefers. 1. for each product of user u without preference i2 for each user of user v3 with preference for product I, the similarity s between user u and v is calculated. // In fact, online computing is n

/* * This program is written to test custom grouplens evaluation * */package Byuser;import Java.io.file;import Org.apache.mahout.cf.taste.common.tasteexception;import Org.apache.mahout.cf.taste.eval.RecommenderBuilder; Import Org.apache.mahout.cf.taste.eval.recommenderevaluator;import Org.apache.mahout.cf.taste.impl.eval.averageabsolutedifferencerecommenderevaluator;import Org.apache.mahout.cf.taste.impl.neighborhood.nearestnuserneighborhood;import Org.apache.mahout.cf.taste.impl.recommender.gen

canopy. Canopy clustering is often used as an initial step for stricter clustering techniques, such as K-means clustering. Through an initial clustering, You can significantly reduce the number of consumed distance measurements by ignoring the points of the initial canopies. The canopy clustering algorithm is often used to pre-process the K-means clustering algorithm to find the appropriate K-value and cluster center. The biggest problem with K-means is that users must give the number of K

Version: hadoop2.4+mahout0.9 When you invoke the Cloud Platform Mahout algorithm in a Web program, you sometimes encounter problems where you cannot find a path, such as the Org.apache.mahout.clustering.classify.ClusterClassifier in the class public void Readfromseqfiles (Configuration conf, path Path) throws IOException { Configuration config = new Configurat Ion (); list More Wonderful content: http://www.bianceng.cnhttp://www.biance

Hadoop Foundation----Hadoop Combat (vi)-----HADOOP management Tools---Cloudera Manager---CDH introduction We have already learned about CDH in the last article, we will install CDH5.8 for the following study. CDH5.8 is now a relatively new version of Hadoop with more than hadoop2.0, and it already contains a number of

Mahout implementation of a gender-based item similarity measurement method genderitemsimilarity

1. Construct Affinity Matrix W 2. The structure of the matrix D 3. Laplace Matrix L 4. Compute the eigenvector fiedler vector corresponding to the second small eigenvalue (spectrum) of the L matrix 5. Using the Fiedler vector as the initial center of the Kmean cluster, the Kmeans cluster Affinity Matrix: W_ij=exp (-(d (S_i,s_j)/2o^2)) d (s_i,s_j) = | | s_i,s_j| |. o for pre-set parameters. Degree matrix: D_ii =sum (w_i) Canonical similarity matrix: d^ ( -1/2) *w*d^ (1/2), i.e.: W (I,J)/(

If we need to add some filtering rules (such as item creation time within one year) when building a recommendation system through mahout, we need to use the Idrescorer interface, which is the source code as follows: Package org.apache.mahout.cf.taste.recommender; /** * The interface sets out two methods that must be implemented: 1.rescore method Function: Defines the logic for a new score. According to the new rule, the item for the specified

Objective: To introduce the use of a common recommendation algorithm (user collaborative filtering).Application scenario: After the XXX project runs for a period of time, there will be a lot of video information in the system, and usually the app gives the user push messages (1-3/day),Then this requires us to push more effectively based on the user's behavioral characteristics.Tool Introduction: The use of mahout collaborative filtering algorithmTest

1. After configuring Maven under Eclipse-jee (the version I choose), add Mahout-core to the Pom.xml depencies, and the version is optional The configuration is then downloaded automatically. will be downloaded by default to/home/cc/.m2/below a folder inside, the file is hidden. Http://blog.sina.com.cn/s/blog_6dc9c7cb0101bmch.html See this blog post for detailed procedures. 2. Add all the jar files under the