GenericUserBasedRecommender推荐过程分析

关于皮尔逊积矩相关系数原理分析我写了一片文章，由于图片太多，上传很麻烦，我就放在了GITHUB,地址：https://github.com/tianbaoxing/hmahout/blob/master/doc/recommder/pearsonCorrelation-%E5%8E%9F%E7%90%86%E5%88%86%E6%9E%90.doc

GenericUserBasedRecommender推荐源码流程图：

GenericUserBasedRecommender中recommend推荐方法源码分析

 @Override
  public List<RecommendedItem> recommend(long userID, int howMany, IDRescorer rescorer) throws TasteException {
    Preconditions.checkArgument(howMany >= 1, "howMany must be at least 1");

    log.debug("Recommending items for user ID '{}'", userID);

    long[] theNeighborhood = neighborhood.getUserNeighborhood(userID);

    if (theNeighborhood.length == 0) {
      return Collections.emptyList();
    }

    FastIDSet allItemIDs = getAllOtherItems(theNeighborhood, userID);

    TopItems.Estimator<Long> estimator = new Estimator(userID, theNeighborhood);

    List<RecommendedItem> topItems = TopItems
        .getTopItems(howMany, allItemIDs.iterator(), rescorer, estimator);

    log.debug("Recommendations are: {}", topItems);
    return topItems;
  }
  

(2) FastIDSet allItemIDs = getAllOtherItems(theNeighborhood, userID); 获取到用户邻居喜欢的主题(去掉自己喜欢的主题)

(3) List<RecommendedItem> topItems = TopItems.getTopItems(howMany, allItemIDs.iterator(), rescorer, estimator);

获取到评分最高的主题,推荐给用户.

现在看看那TopItems.getTopItems(howMany, allItemIDs.iterator(), rescorer, estimator);源码

 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;
  }

主意:IDRescorer就是在这里调用的

(1)   if (rescorer == null || !rescorer.isFiltered(itemID)) 这里主要是判断这个主题是否过滤掉.

(2)preference = estimator.estimate(itemID)计算这个用户喜欢这个主题的评分

(3)topItems.add(new GenericRecommendedItem(itemID, (float) rescoredPref)); 把主题增加到一个集合中.