Java LogLikelihoodSimilarity类代码示例

import org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity; //导入依赖的package包/类
public static void main(String[] args) throws TasteException, IOException {
	DataModel model = new FileDataModel(new File("data/dataset.csv"));
   	ItemSimilarity similarity = new LogLikelihoodSimilarity(model);
   	//UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
   	GenericItemBasedRecommender recommender = new GenericItemBasedRecommender(model, similarity);
   	List<RecommendedItem> recommendations = recommender.mostSimilarItems(18, 3);
   	for (RecommendedItem recommendation : recommendations) {
   	  System.out.println(recommendation);
   	}

}
 

import org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity; //导入依赖的package包/类
/**
 * Get the similarity instance that is used to check which users and items
 * are similar. There are several implementations for this, these are specified
 * by the this.alg value of the RecommendEntityServlet instance.
 * NOTE: Since there are two interfaces: ItemSimilarity and UserSimilarity the
 * returned type is of the interface Refreshable since both interfaces inherit
 * that type as well. Cast it to the right type.
 * @param model The database model to use for the similarity calculations
 * @return The UserSimilarity or ItemSimilarity instance.
 * @throws TasteException If an exception is thrown by Mahout it is forwarded upwards.
 */
private Refreshable getSimularity(DataModel model) throws TasteException {
    if(this.alg.equalsIgnoreCase(A_EUCLIDEAN)){
        return new EuclideanDistanceSimilarity(model);
    } else if(this.alg.equalsIgnoreCase(A_PEARSON)){
        return new PearsonCorrelationSimilarity(model);
    } else if(this.alg.equalsIgnoreCase(A_TANIMOTO)){
        return new TanimotoCoefficientSimilarity(model);
    } else if(this.alg.equalsIgnoreCase(A_LOGLIKELIHOOD) || this.alg == null){ // == null is the default case
        return new LogLikelihoodSimilarity(model);
    } else {
        throw new TasteException("Unknown algorithm type: " + this.alg);
    }
}
 

import org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity; //导入依赖的package包/类
@ResponseBody
	@RequestMapping(value = "/recommend/user/{userId}", method = RequestMethod.GET)
	public String recommend(@PathVariable Long userId
			) throws TasteException, IOException {
		
		DataModel dataModel = getFileDataModel();
		
		
		UserSimilarity userSimilarity =
//				new PearsonCorrelationSimilarity(dataModel); //皮尔逊相关系数
//				new EuclideanDistanceSimilarity(dataModel); //欧氏距离
//				new TanimotoCoefficientSimilarity(dataModel); //谷本系数
				new LogLikelihoodSimilarity(dataModel); //对数似然比
		
//		userSimilarity.setPreferenceInferrer(new AveragingPreferenceInferrer(dataModel));
		
		UserNeighborhood userNeighborhood = 
				new NearestNUserNeighborhood(10, userSimilarity, dataModel);

		
		Recommender recommender = 
				//new GenericUserBasedRecommender(dataModel, userNeighborhood, userSimilarity);
				new SVDRecommender(dataModel, new ALSWRFactorizer(dataModel, 10, 0.05, 10));
		
		List<RecommendedItem> recommendedItems =
				recommender.recommend(userId, 10);
		
		Gson gson = new Gson();
		
		List<RecommendResultVO> recommendResultVOs =
				new ArrayList<RecommendResultVO>(recommendedItems.size());
		
		for (int i = 0; i < recommendedItems.size(); i++) {
			RecommendResultVO recommendResultVO = new RecommendResultVO();
			recommendResultVO.setRestaurantId(recommendedItems.get(i).getItemID());
			recommendResultVO.setScore(recommendedItems.get(i).getValue());
			
			recommendResultVOs.add(i, recommendResultVO);
		}
		
		return gson.toJson(recommendResultVOs);
	}
 

import org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity; //导入依赖的package包/类
@ResponseBody
@RequestMapping(value = "/recommend/user/{userId}/food/{foodId}", method = RequestMethod.GET)
public String recommendByFood(@PathVariable Long userId, @PathVariable Long foodId
		) throws TasteException, IOException {
	
	DataModel dataModel = getFileDataModel();
	
	
	UserSimilarity userSimilarity =
			new LogLikelihoodSimilarity(dataModel); //对数似然比
	
	UserNeighborhood userNeighborhood = 
			new NearestNUserNeighborhood(10, userSimilarity, dataModel);

	Recommender recommender = 
			new SVDRecommender(dataModel, new ALSWRFactorizer(dataModel, 10, 0.05, 10));
	
	List<RecommendedItem> recommendedItems =
			recommender.recommend(userId, 1000);
	
	Gson gson = new Gson();
	
	List<RecommendResultVO> recommendResultVOs =
			new ArrayList<RecommendResultVO>();
	
	for (int i = 0; i < recommendedItems.size(); i++) {
		List<RestaurantFood> rfList = restaurantFoodService.
				getRestaurantFoodsByRestaurantId(recommendedItems.get(i).getItemID());
		
		for (RestaurantFood restaurantFood : rfList) {
			if (restaurantFood.getFoodId() == foodId) {
				RecommendResultVO recommendResultVO = new RecommendResultVO();
				recommendResultVO.setRestaurantId(recommendedItems.get(i).getItemID());
				
				recommendResultVO.setScore(recommendedItems.get(i).getValue());
				
				recommendResultVOs.add(recommendResultVO);
			}
		}
		
	}
	
	return gson.toJson(recommendResultVOs);
}
 

import org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity; //导入依赖的package包/类
/**
 * <b>[推薦の評価方法]</b><br>
 * cf.)70%のデータを使用してレコメンダーを生成→30%のデータを使用して評価
 * 
 * @param dataModel
 * @throws TasteException
 */
@Override
public void analyze() throws TasteException {

    // アイテムの類似性を定義
    super.i("◆ ITEM ======= Start ======");
    super.i("◆ -------------------------");

    try {

        final Map<ItemName, ItemAffinityVO> itemMap = super.dto.itemMap;

        // 谷本係数
        ItemSimilarity a_tanimoto = new TanimotoCoefficientSimilarity(super.dataModel);
        ItemAffinityVO tanimoto = itemMap.get(ItemName.TANIMOTO);
        recommend(super.dataModel, a_tanimoto, tanimoto);

        // シティブロック距離
        ItemSimilarity a_cityBlock = new CityBlockSimilarity(super.dataModel);
        ItemAffinityVO cityBlock = itemMap.get(ItemName.CITY_BLOCK);
        recommend(super.dataModel, a_cityBlock, cityBlock);

        // 稀にしか起こらない事象
        ItemSimilarity a_logLikelihood = new LogLikelihoodSimilarity(super.dataModel);
        ItemAffinityVO logLike = itemMap.get(ItemName.LOG_LIKE);
        recommend(super.dataModel, a_logLikelihood, logLike);

        // ユークリッド距離
        ItemSimilarity a_euclid = new org.apache.mahout.cf.taste.impl.similarity.EuclideanDistanceSimilarity(super.dataModel);
        ItemAffinityVO euclidean = itemMap.get(ItemName.EUCLIDEAN);
        recommend(super.dataModel, a_euclid, euclidean);

        // コサイン類似度
        ItemSimilarity a_cosine = new org.apache.mahout.cf.taste.impl.similarity.UncenteredCosineSimilarity(super.dataModel);
        ItemAffinityVO cosine = itemMap.get(ItemName.COSINE);
        recommend(super.dataModel, a_cosine, cosine);

    } catch (IllegalArgumentException e) {
    }

    super.i("◆ -------------------------");
    super.iln("◆ ITEM ======= END ======");
}
 

import org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity; //导入依赖的package包/类
public static LogLikelihoodSimilarity getLogLikelihoodSimilarity(DataModel dataModel)
		throws TasteException {
	System.out.println("LogLikelihoodSimilarity");
	return new LogLikelihoodSimilarity(dataModel);
}