mahout过滤推荐结果 Recommender.recommend(long userID, int howMany, IDRescorer rescorer)

Recommender.recommend(uid, RECOMMENDER_NUM, rescorer);

Recommender.recommend(long userID,
int howMany, IDRescorer rescorer): 获得推荐结果，给userID推荐howMany个Item，凡rescorer中包含的Item都过滤掉。

其中源码中调用了以下方法 TopItems.getTopItems

TopItems类的.getTopItems

public static List<RecommendedItem> getTopItems(int howMany,
                                                  LongPrimitiveIterator possibleItemIDs,
                                                  IDRescorer rescorer,
                                                  Estimator<Long> estimator) throws TasteException {
    Preconditions.checkArgument(possibleItemIDs != null, "argument is null");
    Preconditions.checkArgument(estimator != null, "argument is null");

    Queue<RecommendedItem> topItems = new PriorityQueue<RecommendedItem>(howMany + 1,
      Collections.reverseOrder(ByValueRecommendedItemComparator.getInstance()));
    boolean full = false;
    double lowestTopValue = Double.NEGATIVE_INFINITY;
    while (possibleItemIDs.hasNext()) {
      long itemID = possibleItemIDs.next();
      if (rescorer == null || !rescorer.isFiltered(itemID)) {
        double preference;
        try {
          preference = estimator.estimate(itemID);
        } catch (NoSuchItemException nsie) {
          continue;
        }
        double rescoredPref = rescorer == null ? preference : rescorer.rescore(itemID, preference);
        if (!Double.isNaN(rescoredPref) && (!full || rescoredPref > lowestTopValue)) {
          topItems.add(new GenericRecommendedItem(itemID, (float) rescoredPref));
          if (full) {
            topItems.poll();
          } else if (topItems.size() > howMany) {
            full = true;
            topItems.poll();
          }
          lowestTopValue = topItems.peek().getValue();
        }
      }
    }
    int size = topItems.size();
    if (size == 0) {
      return Collections.emptyList();
    }
    List<RecommendedItem> result = Lists.newArrayListWithCapacity(size);
    result.addAll(topItems);
    Collections.sort(result, ByValueRecommendedItemComparator.getInstance());
    return result;
  }

recommend(long userID, int howMany): 获得推荐结果，给userID推荐howMany个Item



estimatePreference(long userID, long itemID): 当打分为空，估计用户对物品的打分

setPreference(long userID, long itemID, float value): 赋值用户，物品，打分

removePreference(long userID, long itemID): 删除用户对物品的打分

getDataModel(): 提取推荐数据

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