Java ItemBasedRecommender类代码示例

import org.apache.mahout.cf.taste.recommender.ItemBasedRecommender; //导入依赖的package包/类
@Override
public void run(RecommenderConfiguration configuration,
                Environment environment) {
    
    PGPoolingDataSource pgPoolingDataSource = configuration.getDataSourceFactory().build(environment);
    ReloadFromJDBCDataModel dataModel = null;
    try {
        dataModel = configuration.getDataModelFactory().build(pgPoolingDataSource);
    } catch (TasteException e) {
        System.err.println(e);
        System.exit(-1);
    }
    
    Recommender userBasedRecommender = configuration.getRecommenderFactory().buildUserBasedRecommender(dataModel);
    ItemBasedRecommender itemBasedRecommender = configuration.getRecommenderFactory().buildItemBasedRecommender(dataModel);
    
    final RecommendationResource userRecommendationResource = new RecommendationResource(userBasedRecommender, itemBasedRecommender);
    final DataModelResource dataModelResource = new DataModelResource(dataModel);
    environment.jersey().register(userRecommendationResource);
    environment.jersey().register(dataModelResource);
}
 

import org.apache.mahout.cf.taste.recommender.ItemBasedRecommender; //导入依赖的package包/类
public static ItemBasedRecommender itemBased() throws Exception {

		// Load the data
		StringItemIdFileDataModel dataModel = loadFromFile("data/BX-Book-Ratings.csv", ";");
		// Collection<GenericItemSimilarity.ItemItemSimilarity> correlations =
		// null;
		// ItemItemSimilarity iis = new ItemItemSimilarity(0, 0, 0);
		// ItemSimilarity itemSimilarity = new
		// GenericItemSimilarity(correlations);
		ItemSimilarity itemSimilarity = new PearsonCorrelationSimilarity(dataModel);

		ItemBasedRecommender recommender = new GenericItemBasedRecommender(
				dataModel, itemSimilarity);

		IDRescorer rescorer = new MyRescorer();

		// List recommendations = recommender.recommend(2, 3, rescorer);
		String itemISBN = "042513976X";
		long itemID = dataModel.readItemIDFromString(itemISBN);
		int noItems = 10;

		System.out.println("Recommendations for item: " + books.get(itemISBN));

		System.out.println("\nMost similar items:");
		List<RecommendedItem> recommendations = recommender.mostSimilarItems(
				itemID, noItems);
		for (RecommendedItem item : recommendations) {
			itemISBN = dataModel.getItemIDAsString(item.getItemID());
			System.out.println("Item: " + books.get(itemISBN) + " | Item id: "
					+ itemISBN + " | Value: " + item.getValue());
		}
		
		return recommender;
	}
 

import org.apache.mahout.cf.taste.recommender.ItemBasedRecommender; //导入依赖的package包/类
public static void main(String[] args) throws Exception {
    String base = "C:\\Users\\smallnest\\Desktop\\test\\";
    File file = new File(base + "user_movies.csv");
    DoubanFileDataModel model = new DoubanFileDataModel(file);

    //http://www.cnphp6.com/archives/84955
    //曼哈顿相似度
    //UserSimilarity similarity = new org.apache.mahout.cf.taste.impl.similarity.CityBlockSimilarity(model);
    //欧几里德相似度
    //UserSimilarity similarity = new org.apache.mahout.cf.taste.impl.similarity.EuclideanDistanceSimilarity(model);
    //对数似然相似度
    //UserSimilarity similarity = new org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity(model);
    //斯皮尔曼相似度
    //UserSimilarity similarity = new org.apache.mahout.cf.taste.impl.similarity.SpearmanCorrelationSimilarity(model);
    //Tanimoto 相似度
    //UserSimilarity similarity = new org.apache.mahout.cf.taste.impl.similarity.TanimotoCoefficientSimilarity(model)
    //Cosine相似度
    //UserSimilarity similarity = new org.apache.mahout.cf.taste.impl.similarity.UncenteredCosineSimilarity();

    //皮尔逊相似度
    ItemSimilarity similarity = new PearsonCorrelationSimilarity(model);
    ItemBasedRecommender recommender = new GenericItemBasedRecommender(model, similarity);

    BatchItemSimilarities batch = new MultithreadedBatchItemSimilarities(recommender, 5);
    int numSimilarities = batch.computeItemSimilarities(Runtime.getRuntime().availableProcessors(), 1, new FileSimilarItemsWriter(new File(base + "item_result.csv")));

    System.out.println("Computed " + numSimilarities + " similarities for " + model.getNumItems() + " items " + "and saved them to file " + base + "item_result.csv");
}
 

import org.apache.mahout.cf.taste.recommender.ItemBasedRecommender; //导入依赖的package包/类
/**
 * レコメンデーションを生成して出力
 * 
 * @param dataModel
 * @param algorithm
 * @throws TasteException
 */
private void recommend(DataModel dataModel, ItemSimilarity algorithm, ItemAffinityVO dto) throws TasteException {
    super.i("◆ " + algorithm.getClass());
    ItemBasedRecommender recommender = new GenericItemBasedRecommender(dataModel, algorithm);
    List<RecommendedItem> items = recommender.recommend(dto.userId, dto.howMany);
    for (RecommendedItem item : items) {
        super.i("◆ " + item);
    }
}
 

import org.apache.mahout.cf.taste.recommender.ItemBasedRecommender; //导入依赖的package包/类
@Override
public ItemBasedRecommender buildRecommender(DataModel dataModel) throws TasteException {
    return new GenericBooleanPrefItemBasedRecommender(dataModel, similarity);
}
 

import org.apache.mahout.cf.taste.recommender.ItemBasedRecommender; //导入依赖的package包/类
public RecommendationResource(Recommender u, ItemBasedRecommender i){
    this.userBasedRecommender = u;
    this.itemBasedRecommender = i;
}