mahout hadoop

recommends products similar to the goods in the customer's shopping basket and the products that the customer may be interested in; Email: Recommend the system by e-mail to inform the customer may be interested in commodity information; Comments: The recommendation system provides customers with other customer comments about the product. Introduction to Apache Mahout Apache Mahout is an open source pro

Mahout version: 0.7,hadoop version: 1.0.4,jdk:1.7.0_25 64bit. This chapter begins the series SVD, namely descending dimension. This can be directly run in the Mahout mahout_home/mahout/svd-h to see the algorithm call parameters, or in the official website of the corresponding page can also be seen, the actual use of t

2. Introduction of RecommenderThis chapter outlines:???????? Recommender in Mahout????????? A glimpse of the actual recommender????????? Recommended engine accuracy and quality assessment????????? Test based on a real data set: GrouplensEvery day we make some comments about things we like, dislike or even care about. This behavior is often unconscious. You hear a song on the radio, you may notice it because it is wonderful or nasty, or you can ignore

Mahout version: 0.7,hadoop version: 1.0.4,jdk:1.7.0_25 64bit. In the last blog in the final terminal information can be seen, SVD algorithm a total of 5 job tasks. Following through the Mahout distributedlanczossolver source code to analyze each: In order to facilitate the subsequent data at any time, use Wine.dat modified data, as follows (5 rows, 13 columns):

Item-based recommendation is based on item similarity. In mahout, it means that itemsimilarity is used to implement similarity measurement, rather than usersimilarity. They use similar users and similar items. Item-based: understanding users' preferences and searching for similar items User-based: find similar users and learn what they like. If the number of items is much smaller than the number of users, item-based recommendations will improve perfor

* The mahout vector class does not implement the writable interface. To avoid their direct coupling with Hadoop.* But can use the Vectorwritable class to encapsulate a vector and make it a writable.* That is, vectors in Mahout can be written to Sequencefile using the Vectorwritable class. */public class Applestovectors {public static void main (string[] args) thr

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Using the grouplens dataset UA. Base This is a tab-separated file, user ID, item ID, rating (preference value), and additional information. Available? Previously, the CSV format is used, and now the TSV format is used. It is available and filedatamodel is used. Use this dataset to test the Evaluation Program in mahout recommendation 2: Package mahout; import Java. io. file; import Org. apache.

1. Prepare data: Intro.csv: 1,101, 5.01,102, 3.01,103, 2.5 2,101, 2.02,102, 2.52,103, 5.02,104, 2.0 3,101, 2.53,104, 4.03,105, 4.53,107, 5.0 4,101, 5.04,103, 3.04,104, 4.54,106, 4.0 5,101, 4.05,102, 3.05,103, 2.05,104, 4.05,105, 3.55,106, 4.0 2. Programming implementation: Purpose: To recommend a product for user 1: Package mahout; import Java. io. file; import Java. util. list; import Org. apache. mahout.

Input analysis: Files processed in mahout must be in sequencefile format. Therefore, txtfile must be converted to sequencefile, and clustering must be in vector format. mahout provides the following two commands to convert text to Vector Form.1. mahout seqdirectory: converts a text file to a sequencefile. A sequencefile is a binary key-Value Pair stored in binar

Mahout(1) data bearing Recommendation data is processed on a large scale. In a cluster environment, the data to be processed may be several GB. Therefore, Mahout optimizes recommendation data. Preference In Mahout, user Preference is abstracted as a Preference, including userId, itemId, and Preference value (user's Preference for item ). Preference is an interfac

Public class tmahout03 {public static void main (string [] ARGs) throws ioexception, tasteexception {//-configuration and operation of accuracy and recall rate evaluation -//Randomutils. usetestseed (); datamodel model = new filedatamodel (new file ("path/UA. base "); recommenderirstatsevaluator irstatsevaluator = new genericrecommenderirstatsevaluator (); recommenderbuilder = new recommenderbuilder () {@ override public recommender buildrecommender (datamodel Model) throws tasteexception {users

Recommended data processing is large-scale, the next time in the cluster environment to process the data may be several gigabytes, so mahout for the recommended data optimization. Preference In Mahout, the user's preferences are abstracted as a preference, containing userid,itemid and preference values (user preferences for item). Preference is an interface, and it has a common implementation that is gener

Hadoop family products, commonly used projects include Hadoop, Hive, Pig, HBase, Sqoop, Mahout, Zookeeper, Avro, Ambari, Chukwa, new additions including, YARN, Hcatalog, Oozie, Cassandra, Hama, Whirr, Flume, Bigtop, Crunch, hue, etc. Since 2011, China has entered the era of big data surging, and the family software, represented by

Mahout version: 0.7,hadoop version: 1.0.4,jdk:1.7.0_25 64bit. Look at the SVD judge online surface using the Amazon cloud platform calculation, but given the SVD algorithm to call the way, when the eigenvectors, what should be done? For example, the original data is 600*60 (600 rows, 60 columns) of data, calculated eigenvectors is 24*60 (of which 24 is not the rank of a value), then the final result should

java.util.*; public class Recommenderintro {private Recommenderintro () {}; public static void Main (String args[]) throws exception{//step:1 Build Model 2 compute similarity 3 find K close proximity 4 construct recommendation engine Datamod El model =new Filedatamodel (new file ("/home/test/test-in/test.txt");//filename must be absolute path usersimilarity similarity =new Pear Soncorrelationsimilarity (model); Userneighborhood Neighborhood =new Nearestnuserneighborhood (2,similarity,mod

Mahout version: 0.7,hadoop version: 1.0.4,jdk:1.7.0_25 64bit. This article analyzes whether the analysis is correct, mainly to write the last output file read and add log information printing related variables. First, write the following test file to analyze all the output: Package Mahout.fansy.item; Import java.io.IOException; Import Java.util.Map; Import Mahout.fansy.utils.read.ReadArbiKV; Import org.a

the center value of the class by means of the mean value; (4) For all the C clustering centers, if the value remains unchanged after iteration (2) (3), the iteration ends. Otherwise, the iteration continues. The biggest advantage of this algorithm is its simplicity and speed. The key of an algorithm is the initial Center Selection and distance formula. II. Implementation of mahout kmeans clustering:(1) The input parameter specifies all data points to

Mahout version: 0.7,hadoop version: 1.0.4,jdk:1.7.0_25 64bit. 1. Job Posts Next, analyze the Run method of Eigenverificationjob: public int Run (path corpusinput, path eigeninput, path output, P Ath Tempout, double maxerror, Double Mineigenvalue, Boolean Inmem Ory, Configuration conf) throws IOException {this.outpath = output; This.tmpout = Tempout; This.maxerror = Maxerror;

① Download the latest mahout version from the official website and put it in the/usr/local/directory of the Linux local system. Unzip the package.Tar-zxvf mahout-distribution-0.9.tar.gz② Rename the decompressed folder as mahoutMV mahout-distribution-0.9 mahout③ ExecutionVI/etc/profileConfigure the