谁有基于用户的推荐系统或者协同过滤的算法和代码分析啊

槽槽 2014-08-08 01:57:20

求个大数据的大神给个基于用户的推荐系统或者协同过滤的算法和代码分析啊
我有部分代码但是不知道怎么在Eclipse上实现求解答啊
1.public class AggregateAndRecommendReducer extends Reducer<VarLongWritable,VectorWritable,VarLongWritable,RecommendedItemsWritable>{
...
public viod reduce (VarLongWritable key,Iterable<VectorWritable>values,Context context)throws IOException,InterruptedException{
Vector recommendationVector=null;
for(VectorWritable vectorWritable:values){
recommendationVector=recommendationVector==null?
vectorWritable.get();
recommendationVector.plus(bectorWritable.get());
}
Queue<RecommendedItem> topItems=new PriorityQueue<RecommendedItem>(recommendationsPerUser+1,Collections.reverseOrder(ByValueRecommendedItemComparator.getInstance()));
Iterator<Vector.Element> recommendationVectorIterator=recommendationVector.iterateNonZero();
while(recommendationVectorIterator.hasNext()){
vector.Element element=recommendationVectorIterator.next();
int index=element.index();
float value = (float) element.get();
if (topItems.size()<recommendationsPerUser){
topItems.add(new GenericRecommendedItem(indexItemIDMap.get(index),value));
}
else if (value>topItems.peek().getValue()){
topItems.add(new GenericRecommendedItem(indexItemIDMap.get(index),value));
topItems.poll();
}
}
List<RecommendedItem> recommendations=new ArrayList<RecommendedItem>(topItems.size());
recommendations.addAll(topItems);
Collections.sort(recommendations,ByValueRecommendedItemComparator.getInstance());
context.write(key,
new
recommendedItemsWritable(recommendations));
}
}
2.public class AggregateCombiner extends Reducer<VarLongWritable,VectorWritable,VarLongWritable,VectorWritable>{public void reduce(varLongWritable key,Iterable<VectorWritable>values,Context context)throws IOException, IniterruptedException{
Vector partial=null;
for (VectorWritable vectorWritable:values){
partial=partial==null?
vectorWritable.get():partial.plus(vectorWritable.get());
}
context.write(key,new VectorWritable(partial));
}
}
3.public class CooccurrenceColumnWrapperMapper extends Mapper<IntWritable,VectorWritable,IntWritable,VectorOrPrefWritable>{
public void map(IntWritable key,VectorWritable value,Context context) throws IOException, IniterruptedException{
context.write(key,new VectorOrPrefWritable(value.get()));
}
}
4.public class PartialMultiplyMapper extends Mapper<IntWritable,VectorAndPrefsWritable,VarLongWritable,VectorWritable>{
public void map(IntWritable key,VectorAndPrefsWritable vectorAndPrefsWritable,Context context)throws IOException, InterruptedException{
Vector cooccurrenceColumn=vectorAndPrefsWritable.getVector();
List<Long> userIDs=vectorAndPrefsWritable.getUserIDs();
List<Float> prefValues=vectorAndPrefsWritable.getValues();
for (int i=0;i<=userIDs.size();i++){
long userID=userIDs.get(i);
float prefValue=prefValues.get(i);
Vector partialProduct=cooccurrenceColumn.times(prefValue);
context.write(new VarLongWritable(userID),new VectorWritable(partialProduct));
}
}
}
5.public class UserVectorSplitterMapper extends Mapper<VarLongWritable,VectorWritable,IntWritable,VectorOrPrefWritable>{
public void map(VarLongWritable key,VectorWritable key,Context context)throws IOException, InterruptedException{
long userID=key.get();
Vector userVector = value.get();
Iterator<Vector.Element>it=userVector.iterateNonZero();
IntWritable itemIndexWritable=new IntWritable();
while(it.hasNext()){
vector.Element e = it,next();
int itemIndex=e.index();
float preferenceValue=(float)e.get();
itemIndexWritable.set(itemIndex);
context.write(itemIndexWritable,new VectorOrPrefWritable(userID,preferenceValue));
}
}
}
6.public UserVectorToCooccurrenceMapper extends Mapper<VarLongWritable,VarLongWritable,Intwritable,Intwritable>{
public void map(VarLongWritable userID,VectorWritable userVector,Context context) throws IOException, InterruptedException{Iterator<Vector.Element> it=userVector.get().iterateNonZero();
while(it.hasNext()){
int index1 = it.next().index();
Iterator<Vector,Element> it2 = userVector.get().iterateNonZero();
while(it2.hasNext()){
int index2 = it2.next(0.index();
context.write(new IntWritable(index1),new IntWritable(index2));
}
}
}
7.
public UserVectorToCooccurrenceReducer extends Reducer<Intwritable,Intwritable,Intwritable,VectorWritable>{
public void reduce(Intwritable itemIndex1,Iterable<IntWritable>itemIndex2s,Context context) throws IOException, InterruptedException{
Vector cooccurrenceRow=new RandomAccessSparseVector(Integer.MAX_VALUE,100);
FOR (Intwritable intwritable:itemIndex2s){
int itemIndex2 = intwritable.get();
cooccurrenceRow.set(itemIndex2,cooccurrenceRow.get(itemIndex2)+1.0);
}
context.write(itemIndex1,new VectorWritable(cooccurrenceRow));
}
}
8.public class WikipediaToItemPrefsMapper
extends Mapper<LongWritable,Text,VarLongWritable,VarLongWritable>{
pricate static final pattern NUMBERS=Pattern.compile("(\\d+)");
public void map(LongWritable key,Text value,Context context)throws TOException, InterruptedException{
String line = value.toString();
Matcher m = NIMBERS.matcher(line);
m.find();
VarLongWritable userID=new VarLongWritable(Long.parseLong(m.group()));
VarLongWritable itemID=new VarLongWritable()
while(m.find()){
itemID.set(Long.parseLong(m.group()));
context.write(userID,itemID);
}
}
}
9.public class WikipediaToUserPrefsReducer
extends Reducer<VarLongWritable,VarLongWritable,VarLongWritable,VectorWritable>{
public void reduce(VarLongWritable userID,Iterable<VarLongWritable> itemPrefs,Context context)throws TOException, InterruptedException{
Vector userVector=new RandomAccessSparseVector(Integer.MAX_VALUE,100);
//*循环遍历用户的物品偏好对*//
for (VarLongWritable itemPref:itemPrefs){
userVector.set((int)itemPref.get(),1.0f);
}
context.write(userID,new VectorWritable(userVector));
}
}

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