先来看一下使用流程:
1)拿到DataModel
2)定义相似度计算模型 PearsonCorrelationSimilarity
3)定义用户邻域计算模型 NearestNUserNeighborhood
4)定义推荐模型 GenericUserBasedRecommender
5)进行推荐
  @Test
public void testHowMany() throws Exception {
DataModel dataModel = getDataModel(
new long[] {1, 2, 3, 4, 5},
new Double[][] {
{0.1, 0.2},
{0.2, 0.3, 0.3, 0.6},
{0.4, 0.4, 0.5, 0.9},
{0.1, 0.4, 0.5, 0.8, 0.9, 1.0},
{0.2, 0.3, 0.6, 0.7, 0.1, 0.2},
});
//用于计算最相似的用户,领域用户
UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, dataModel); Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
List<RecommendedItem> fewRecommended = recommender.recommend(1, 2);
List<RecommendedItem> moreRecommended = recommender.recommend(1, 4);
for (int i = 0; i < fewRecommended.size(); i++) {
assertEquals(fewRecommended.get(i).getItemID(), moreRecommended.get(i).getItemID());
}
recommender.refresh(null);
for (int i = 0; i < fewRecommended.size(); i++) {
assertEquals(fewRecommended.get(i).getItemID(), moreRecommended.get(i).getItemID());
}
}

相似度计算,参考上篇的PearsonCorrelationSimilarity。

NearestNUserNeighborhood ,获取最近的N个用户,怎么实现的呢?
~/mahout-core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/GenericUserBasedRecommender.java
  @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); //根据similarity模型进行计算,计算最相似的N个用户
long[] theNeighborhood = neighborhood.getUserNeighborhood(userID); if (theNeighborhood.length == 0) {
return Collections.emptyList();
}
//获取其他领域用户进行评分而且当前用户所没有进行评分的Item列表,作为推荐的基本池子
FastIDSet allItemIDs = getAllOtherItems(theNeighborhood, userID); //获取池子里面,当前用户偏好最高的TopN进行推荐
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;
}
Estimator的实现,是这样的:
  private final class Estimator implements TopItems.Estimator<Long> {

    private final long theUserID;
private final long[] theNeighborhood; Estimator(long theUserID, long[] theNeighborhood) {
this.theUserID = theUserID;
this.theNeighborhood = theNeighborhood;
} @Override
public double estimate(Long itemID) throws TasteException {
return doEstimatePreference(theUserID, theNeighborhood, itemID);
}
}
}
 
  protected float doEstimatePreference(long theUserID, long[] theNeighborhood, long itemID) throws TasteException {
//把相似用户对该Item的偏好累加起来,再做平均值,当做当前用户对改Item的偏好
if (theNeighborhood.length == 0) {
return Float.NaN;
}
DataModel dataModel = getDataModel();
double preference = 0.0;
double totalSimilarity = 0.0;
int count = 0;
for (long userID : theNeighborhood) {
if (userID != theUserID) {
// See GenericItemBasedRecommender.doEstimatePreference() too
Float pref = dataModel.getPreferenceValue(userID, itemID);
if (pref != null) {
double theSimilarity = similarity.userSimilarity(theUserID, userID);
if (!Double.isNaN(theSimilarity)) {
preference += theSimilarity * pref;
totalSimilarity += theSimilarity;
count++;
}
}
}
}
// Throw out the estimate if it was based on no data points, of course, but also if based on
// just one. This is a bit of a band-aid on the 'stock' item-based algorithm for the moment.
// The reason is that in this case the estimate is, simply, the user's rating for one item
// that happened to have a defined similarity. The similarity score doesn't matter, and that
// seems like a bad situation.
if (count <= 1) {
return Float.NaN;
}
float estimate = (float) (preference / totalSimilarity);
if (capper != null) {
estimate = capper.capEstimate(estimate);
}
return estimate;
}
总结:
1)计算最相似的N个用户
2)从最相似的N个用户中,获取自己没有评分过的Item
3)预计自己对每个Item的偏好
4)取偏好最高的N个Item进行推荐

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