import org.apache.mahout.cf.taste.impl.recommender.CachingRecommender; //导入依赖的package包/类
private static void recommend(String ratingsFile, int ... userIds)
    throws TasteException, IOException {
  DataModel model = new FileDataModel(new File(ratingsFile));

  UserSimilarity similarity = new PearsonCorrelationSimilarity(model);

  UserNeighborhood neighborhood =
      new NearestNUserNeighborhood(
          100, similarity, model);

  Recommender recommender =  new GenericUserBasedRecommender(
      model, neighborhood, similarity);

  Recommender cachingRecommender = new CachingRecommender(recommender);

  for(int userId: userIds) {
    System.out.println("UserID " + userId);
    List<RecommendedItem> recommendations =
        cachingRecommender.recommend(userId, 2);
    for(RecommendedItem item: recommendations) {
      System.out.println("  item " + item.getItemID() + " score " + item.getValue());
    }
  }
}
 

import org.apache.mahout.cf.taste.impl.recommender.CachingRecommender; //导入依赖的package包/类
public static void main( String[] args ) throws IOException, TasteException, OptionException
{
    CreateCsvRatingsFile();


    // create data source (model) - from the csv file
    File ratingsFile = new File(outputFile);
    DataModel model = new FileDataModel(ratingsFile);

    // create a simple recommender on our data
    CachingRecommender cachingRecommender = new CachingRecommender(new SlopeOneRecommender(model));

    // for all users
    for (LongPrimitiveIterator it = model.getUserIDs(); it.hasNext();){
        long userId = it.nextLong();

        // get the recommendations for the user
        List<RecommendedItem> recommendations = cachingRecommender.recommend(userId, 10);

        // if empty write something
        if (recommendations.size() == 0){
            System.out.print("User ");
            System.out.print(userId);
            System.out.println(": no recommendations");
        }

        // print the list of recommendations for each
        for (RecommendedItem recommendedItem : recommendations) {
            System.out.print("User ");
            System.out.print(userId);
            System.out.print(": ");
            System.out.println(recommendedItem);
        }
    }
}
 

import org.apache.mahout.cf.taste.impl.recommender.CachingRecommender; //导入依赖的package包/类
public List<RecommendedItem> recommend(int userid,int size) throws TasteException, IOException{
		List<RecommendedItem> list = null;
//		MovieDataModel model = new MovieDataModel();
		String file=ServletActionContext.getServletContext().getRealPath("/u1.base");
		DataModel model = new FileDataModel(new File(file));
		Recommender recommender = new CachingRecommender(new SlopeOneRecommender(model));
		list = recommender.recommend(userid, size);
		return list;
	}
 

import org.apache.mahout.cf.taste.impl.recommender.CachingRecommender; //导入依赖的package包/类
/**
 * This function will (re)construct all the recommendations using
 * Mahout.
 * NOTE: Be aware that this is a very expensive function in terms of
 * processing time, therefore this function is called on a regular
 * interval (say every x hours) to update the recommendation cache
 * according to the latest data available in the database.
 */
public void reconstructRecommendationCache(){
    long startTime = System.currentTimeMillis();

    try {
        /* Open the database and update the dictionary to Mahout style */
        convertUIDToBigIntInDB();

        /*
         * Database is converted, generate the recommendations seperately
         * from the currently cached recommendations if there are any.
         */
        MySQLJDBCDataModel model = getMahoutDatabaseModel();

        /*
         * Build the recommender itself.
         */
        Recommender userRecommender = null;
        Recommender itemRecommender = null;
        if(this.type == null || this.type.equalsIgnoreCase(T_USER) || this.type.equalsIgnoreCase(T_BOTH)){
            userRecommender = buildRecommenderInstance(model, RecFieldType.USER);
        }
        if(this.type == null || this.type.equalsIgnoreCase(T_ITEM) || this.type.equalsIgnoreCase(T_BOTH)){
            itemRecommender = buildRecommenderInstance(model, RecFieldType.ITEM);
        }

        /*
         * The recommendation process has finished, replace the cached
         * recommendations with the new updated version.
         */
        if(this.type == null || this.type.equalsIgnoreCase(T_USER) || this.type.equalsIgnoreCase(T_BOTH)){
            this.userRecommenderCache = new CachingRecommender(userRecommender);
        }
        if(this.type == null || this.type.equalsIgnoreCase(T_ITEM) || this.type.equalsIgnoreCase(T_BOTH)){
            this.itemRecommenderCache = new CachingRecommender(itemRecommender);
        }
        this.lastExecutionTime = System.currentTimeMillis() - startTime;
        org.webdsl.logging.Logger.info("Building the Mahout Index took: " + this.lastExecutionTime);
    } catch (Exception e){
        org.webdsl.logging.Logger.error("\n\n************ SEVERE ERROR ************\nThere was an exception while reconstructing the recommendations!\n" + e);
        org.webdsl.logging.Logger.error("EXCEPTION",e);
        org.webdsl.logging.Logger.error("************ END OF ERROR ************\n\n");
    }
}