The mahout of recommendation engine based on user collaborative filtering algorithm

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 algorithm

Test code:

/** *  *  based on user's neighbor collaborative filtering recommendation algorithm, *  This paper aims to provide some reference algorithms for the recommended algorithm of XXX follow-up advertisement  *  *  @ All rights reserved: Come to Yi Finance   copyright   (c)  2015 *  @author  feihu.wang *  @version   revision 1.0.0 *  @see:  * @ Date Created: May 18, 2015  * @ feature description:  * */public  class cftest {    public static void main (String[]  args)  throws IOException, TasteException {                   DataModel model = new  Filedatamodel (New file ("E:\\projects\\code\\mahout_test\\intro.csv"));                   //user acquaintance degree  : Pearson relevance myopia          //UserSimilarity sim = new  Pearsoncorrelationsimilarity (model);                  //user acquaintance degree  : European distance          usersimilarity sim = new euclideandistancesimilarity (model);                 //  Nearest Neighbor algorithm         UserNeighborhood nbh = new  Nearestnuserneighborhood (2, sim, model);                     //  generate recommendation engine   :  user-based collaborative filtering algorithm,          //also has an item-based filtering algorithm,mahout  There are many implementations below          recommender rec = new genericuserbasedrecommender (model,  nbh, sim);                     // Recommended items for user ID (1) (Qty 2)           List<RecommendedItem>  recitemlist = rec.recommend (1, 2);                     for (recommendeditem item :  Recitemlist)  {               SYSTEM.OUT.PRINTLN (item);          }                 }

Test data:

Intro.csv

Pom dependency:

<dependency><groupid>org.apache.mahout</groupid><artifactid>mahout-core</artifactid ><version>0.9</version></dependency><dependency><groupid>org.apache.mahout </groupid><artifactid>mahout-math</artifactid><version>0.9</version></ Dependency>

This article from "Wandering Footsteps" blog, declined reprint!

The mahout of recommendation engine based on user collaborative filtering algorithm