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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