mahout 推薦系統
用Mahout來構建推薦系統,是一件既簡單又困難的事情。簡單是因為Mahout完整地封裝了“協同過濾”演算法,並實現了並行化,提供非常簡單的API介面;困難是因為我們不瞭解演算法細節,很難去根據業務的場景進行演算法配置和調優。
本文將深入演算法API去解釋Mahout推薦演算法底層的一些事。
目錄
- Mahout推薦演算法介紹
- 演算法評判標準:召回率與準確率
- Recommender.java的API介面
- 測試程式:RecommenderTest.java
- 基於使用者的協同過濾演算法UserCF
- 基於物品的協同過濾演算法ItemCF
- SlopeOne演算法
- KNN Linear interpolation item–based推薦演算法
- SVD推薦演算法
- Tree Cluster-based 推薦演算法
- Mahout推薦演算法總結
1. Mahout推薦演算法介紹
Mahoutt推薦演算法,從資料處理能力上,可以劃分為2類:
- 單機記憶體演算法實現
- 基於Hadoop的分步式演算法實現
1). 單機記憶體演算法實現
單機記憶體演算法實現:就是在單機下執行的演算法,是由cf.taste專案實現的,像我的們熟悉的UserCF,ItemCF都支援單機記憶體執行,並且引數可以靈活配置。單機演算法的基本例項,請參考文章:用Maven構建Mahout專案
單機記憶體演算法的問題在於,受限於單機的資源。對於中等規模的資料,像1G,10G的資料量,有能力進行計算,但是超過100G的資料量,對於單機來說是不可能完成的任務。
2). 基於Hadoop的分步式演算法實現
基於Hadoop的分步式演算法實現:就是把單機記憶體演算法並行化,把任務分散到多臺計算機一起執行。Mahout提供了ItemCF基於Hadoop並行化演算法實現。基於Hadoop的分步式演算法實現,請參考文章:
Mahout分步式程式開發 基於物品的協同過濾ItemCF
分步式並行演算法的問題在於,如何讓單機演算法並行化。在單機演算法中,我們只需要考慮演算法,資料結構,記憶體,CPU就夠了,但是分步式演算法還要額外考慮很多的情況,比如多節點的資料合併,資料排序,網路通訊的效率,節點宕機重算,資料分步式儲存等等的很多問題。
2. 演算法評判標準:召回率(recall)與查準率(precision)
Mahout提供了2個評估推薦器的指標,查準率和召回率(查全率),這兩個指標是搜尋引擎中經典的度量方法。
相關 不相關
檢索到 A C
未檢索到 B D
- A:檢索到的,相關的 (搜到的也想要的)
- B:未檢索到的,但是相關的 (沒搜到,然而實際上想要的)
- C:檢索到的,但是不相關的 (搜到的但沒用的)
- D:未檢索到的,也不相關的 (沒搜到也沒用的)
被檢索到的越多越好,這是追求“查全率”,即A/(A+B),越大越好。
被檢索到的,越相關的越多越好,不相關的越少越好,這是追求“查準率”,即A/(A+C),越大越好。
在大規模資料集合中,這兩個指標是相互制約的。當希望索引出更多的資料的時候,查準率就會下降,當希望索引更準確的時候,會索引更少的資料。
3. Recommender的API介面
1). 系統環境:
- Win7 64bit
- Java 1.6.0_45
- Maven 3
- Eclipse Juno Service Release 2
- Mahout 0.8
- Hadoop 1.1.2
2). Recommender介面檔案:
org.apache.mahout.cf.taste.recommender.Recommender.Java
介面中方法的解釋:
- recommend(long userID, int howMany): 獲得推薦結果,給userID推薦howMany個Item
- recommend(long userID, int howMany, IDRescorer rescorer): 獲得推薦結果,給userID推薦howMany個Item,可以根據rescorer對結構重新排序。
- estimatePreference(long userID, long itemID): 當打分為空,估計使用者對物品的打分
- setPreference(long userID, long itemID, float value): 賦值使用者,物品,打分
- removePreference(long userID, long itemID): 刪除使用者對物品的打分
- getDataModel(): 提取推薦資料
通過Recommender介面,我可以猜出核心演算法,應該會在子類的estimatePreference()方法中進行實現。
3). 通過繼承關係到Recommender介面的子類:
推薦演算法實現類:
- GenericUserBasedRecommender: 基於使用者的推薦演算法
- GenericItemBasedRecommender: 基於物品的推薦演算法
- KnnItemBasedRecommender: 基於物品的KNN推薦演算法
- SlopeOneRecommender: Slope推薦演算法
- SVDRecommender: SVD推薦演算法
- TreeClusteringRecommender:TreeCluster推薦演算法
下面將分別介紹每種演算法的實現。
4. 測試程式:RecommenderTest.java
測試資料集:item.csv
1,101,5.0
1,102,3.0
1,103,2.5
2,101,2.0
2,102,2.5
2,103,5.0
2,104,2.0
3,101,2.5
3,104,4.0
3,105,4.5
3,107,5.0
4,101,5.0
4,103,3.0
4,104,4.5
4,106,4.0
5,101,4.0
5,102,3.0
5,103,2.0
5,104,4.0
5,105,3.5
5,106,4.0
測試程式:org.conan.mymahout.recommendation.job.RecommenderTest.java
package org.conan.mymahout.recommendation.job;
import java.io.IOException;
import java.util.List;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.eval.RecommenderBuilder;
import org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.common.RandomUtils;
public class RecommenderTest {
final static int NEIGHBORHOOD_NUM = 2;
final static int RECOMMENDER_NUM = 3;
public static void main(String[] args) throws TasteException, IOException {
RandomUtils.useTestSeed();
String file = "datafile/item.csv";
DataModel dataModel = RecommendFactory.buildDataModel(file);
slopeOne(dataModel);
}
public static void userCF(DataModel dataModel) throws TasteException{}
public static void itemCF(DataModel dataModel) throws TasteException{}
public static void slopeOne(DataModel dataModel) throws TasteException{}
...
每種演算法都一個單獨的方法進行演算法測試,如userCF(),itemCF(),slopeOne()….
5. 基於使用者的協同過濾演算法UserCF
基於使用者的協同過濾,通過不同使用者對物品的評分來評測使用者之間的相似性,基於使用者之間的相似性做出推薦。簡單來講就是:給使用者推薦和他興趣相似的其他使用者喜歡的物品。
舉例說明:
基於使用者的 CF 的基本思想相當簡單,基於使用者對物品的偏好找到相鄰鄰居使用者,然後將鄰居使用者喜歡的推薦給當前使用者。計算上,就是將一個使用者對所有物品的偏好作為一個向量來計算使用者之間的相似度,找到 K 鄰居後,根據鄰居的相似度權重以及他們對物品的偏好,預測當前使用者沒有偏好的未涉及物品,計算得到一個排序的物品列表作為推薦。圖 2 給出了一個例子,對於使用者 A,根據使用者的歷史偏好,這裡只計算得到一個鄰居 – 使用者 C,然後將使用者 C 喜歡的物品 D 推薦給使用者 A。
演算法API: org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender
@Override
public float estimatePreference(long userID, long itemID) throws TasteException {
DataModel model = getDataModel();
Float actualPref = model.getPreferenceValue(userID, itemID);
if (actualPref != null) {
return actualPref;
}
long[] theNeighborhood = neighborhood.getUserNeighborhood(userID);
return doEstimatePreference(userID, theNeighborhood, itemID);
}
protected float doEstimatePreference(long theUserID, long[] theNeighborhood, long itemID) throws TasteException {
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;
}
測試程式:
public static void userCF(DataModel dataModel) throws TasteException {
UserSimilarity userSimilarity = RecommendFactory.userSimilarity(RecommendFactory.SIMILARITY.EUCLIDEAN, dataModel);
UserNeighborhood userNeighborhood = RecommendFactory.userNeighborhood(RecommendFactory.NEIGHBORHOOD.NEAREST, userSimilarity, dataModel, NEIGHBORHOOD_NUM);
RecommenderBuilder recommenderBuilder = RecommendFactory.userRecommender(userSimilarity, userNeighborhood, true);
RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
LongPrimitiveIterator iter = dataModel.getUserIDs();
while (iter.hasNext()) {
long uid = iter.nextLong();
List list = recommenderBuilder.buildRecommender(dataModel).recommend(uid, RECOMMENDER_NUM);
RecommendFactory.showItems(uid, list, true);
}
}
程式輸出:
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:1.0
Recommender IR Evaluator: [Precision:0.5,Recall:0.5]
uid:1,(104,4.333333)(106,4.000000)
uid:2,(105,4.049678)
uid:3,(103,3.512787)(102,2.747869)
uid:4,(102,3.000000)
6. 基於物品的協同過濾演算法ItemCF
基於item的協同過濾,通過使用者對不同item的評分來評測item之間的相似性,基於item之間的相似性做出推薦。簡單來講就是:給使用者推薦和他之前喜歡的物品相似的物品。
舉例說明:
基於物品的 CF 的原理和基於使用者的 CF 類似,只是在計算鄰居時採用物品本身,而不是從使用者的角度,即基於使用者對物品的偏好找到相似的物品,然後根據使用者的歷史偏好,推薦相似的物品給他。從計算的角度看,就是將所有使用者對某個物品的偏好作為一個向量來計算物品之間的相似度,得到物品的相似物品後,根據使用者歷史的偏好預測當前使用者還沒有表示偏好的物品,計算得到一個排序的物品列表作為推薦。圖 3 給出了一個例子,對於物品 A,根據所有使用者的歷史偏好,喜歡物品 A 的使用者都喜歡物品 C,得出物品 A 和物品 C 比較相似,而使用者 C 喜歡物品 A,那麼可以推斷出使用者 C 可能也喜歡物品 C。
演算法API: org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommender
@Override
public float estimatePreference(long userID, long itemID) throws TasteException {
PreferenceArray preferencesFromUser = getDataModel().getPreferencesFromUser(userID);
Float actualPref = getPreferenceForItem(preferencesFromUser, itemID);
if (actualPref != null) {
return actualPref;
}
return doEstimatePreference(userID, preferencesFromUser, itemID);
}
protected float doEstimatePreference(long userID, PreferenceArray preferencesFromUser, long itemID)
throws TasteException {
double preference = 0.0;
double totalSimilarity = 0.0;
int count = 0;
double[] similarities = similarity.itemSimilarities(itemID, preferencesFromUser.getIDs());
for (int i = 0; i < similarities.length; i++) {
double theSimilarity = similarities[i];
if (!Double.isNaN(theSimilarity)) {
// Weights can be negative!
preference += theSimilarity * preferencesFromUser.getValue(i);
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;
}
測試程式:
public static void itemCF(DataModel dataModel) throws TasteException {
ItemSimilarity itemSimilarity = RecommendFactory.itemSimilarity(RecommendFactory.SIMILARITY.EUCLIDEAN, dataModel);
RecommenderBuilder recommenderBuilder = RecommendFactory.itemRecommender(itemSimilarity, true);
RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
LongPrimitiveIterator iter = dataModel.getUserIDs();
while (iter.hasNext()) {
long uid = iter.nextLong();
List list = recommenderBuilder.buildRecommender(dataModel).recommend(uid, RECOMMENDER_NUM);
RecommendFactory.showItems(uid, list, true);
}
}
程式輸出:
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:0.8676552772521973
Recommender IR Evaluator: [Precision:0.5,Recall:1.0]
uid:1,(105,3.823529)(104,3.722222)(106,3.478261)
uid:2,(106,2.984848)(105,2.537037)(107,2.000000)
uid:3,(106,3.648649)(102,3.380000)(103,3.312500)
uid:4,(107,4.722222)(105,4.313953)(102,4.025000)
uid:5,(107,3.736842)
7. SlopeOne演算法
這個演算法在mahout-0.8版本中,已經被@Deprecated。
SlopeOne是一種簡單高效的協同過濾演算法。通過均差計算進行評分。SlopeOne論文下載(PDF)
1). 舉例說明:
使用者X,Y,Z,對於物品A,B進行打分,如下表,求Z對B的打分是多少?
Slope one演算法認為:平均值可以代替某兩個未知個體之間的打分差異,事物A對事物B的平均差是:((5 - 4) + (4 - 2)) / 2 = 1.5,就得到Z對B的打分是,3-1.5 = 1.5。
Slope one演算法將使用者的評分之間的關係看作簡單的線性關係:
Y = mX + b
2). 平均加權計算:
使用者X,Y,Z,對於物品A,B,C進行打分,如下表,求Z對A的打分是多少?
- 1. 計算A和B的平均差, ((5-3)+(3-4))/2=0.5
- 2. 計算A和C的平均差, (5-2)/1=3
- 3. Z對A的評分,通過AB得到, 2+0.5=2.5
- 4. Z對A的評分,通過AC得到,5+3=8
- 5. 通過加權平均計算Z對A的評分:A和B都有評價的使用者數為2,A和C都有評價的使用者數為1,權重為別是2和1, (2*2.5+1*8)/(2+1)=13/3=4.33
通過這種簡單的方式,我們可以快速計算出一個評分項,完成推薦過程!
演算法API: org.apache.mahout.cf.taste.impl.recommender.slopeone.SlopeOneRecommender
@Override
public float estimatePreference(long userID, long itemID) throws TasteException {
DataModel model = getDataModel();
Float actualPref = model.getPreferenceValue(userID, itemID);
if (actualPref != null) {
return actualPref;
}
return doEstimatePreference(userID, itemID);
}
private float doEstimatePreference(long userID, long itemID) throws TasteException {
double count = 0.0;
double totalPreference = 0.0;
PreferenceArray prefs = getDataModel().getPreferencesFromUser(userID);
RunningAverage[] averages = diffStorage.getDiffs(userID, itemID, prefs);
int size = prefs.length();
for (int i = 0; i < size; i++) {
RunningAverage averageDiff = averages[i];
if (averageDiff != null) {
double averageDiffValue = averageDiff.getAverage();
if (weighted) {
double weight = averageDiff.getCount();
if (stdDevWeighted) {
double stdev = ((RunningAverageAndStdDev) averageDiff).getStandardDeviation();
if (!Double.isNaN(stdev)) {
weight /= 1.0 + stdev;
}
// If stdev is NaN, then it is because count is 1. Because we're weighting by count,
// the weight is already relatively low. We effectively assume stdev is 0.0 here and
// that is reasonable enough. Otherwise, dividing by NaN would yield a weight of NaN
// and disqualify this pref entirely
// (Thanks Daemmon)
}
totalPreference += weight * (prefs.getValue(i) + averageDiffValue);
count += weight;
} else {
totalPreference += prefs.getValue(i) + averageDiffValue;
count += 1.0;
}
}
}
if (count <= 0.0) {
RunningAverage itemAverage = diffStorage.getAverageItemPref(itemID);
return itemAverage == null ? Float.NaN : (float) itemAverage.getAverage();
} else {
return (float) (totalPreference / count);
}
}
測試程式:
public static void slopeOne(DataModel dataModel) throws TasteException {
RecommenderBuilder recommenderBuilder = RecommendFactory.slopeOneRecommender();
RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
LongPrimitiveIterator iter = dataModel.getUserIDs();
while (iter.hasNext()) {
long uid = iter.nextLong();
List list = recommenderBuilder.buildRecommender(dataModel).recommend(uid, RECOMMENDER_NUM);
RecommendFactory.showItems(uid, list, true);
}
}
程式輸出:
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:1.3333333333333333
Recommender IR Evaluator: [Precision:0.25,Recall:0.5]
uid:1,(105,5.750000)(104,5.250000)(106,4.500000)
uid:2,(105,2.286115)(106,1.500000)
uid:3,(106,2.000000)(102,1.666667)(103,1.625000)
uid:4,(105,4.976859)(102,3.509071)
8. KNN Linear interpolation item–based推薦演算法
這個演算法在mahout-0.8版本中,已經被@Deprecated。
演算法來自論文:
This algorithm is based in the paper of Robert M. Bell and Yehuda Koren in ICDM '07.
(TODO未完)
演算法API: org.apache.mahout.cf.taste.impl.recommender.knn.KnnItemBasedRecommender
@Override
protected float doEstimatePreference(long theUserID, PreferenceArray preferencesFromUser, long itemID)
throws TasteException {
DataModel dataModel = getDataModel();
int size = preferencesFromUser.length();
FastIDSet possibleItemIDs = new FastIDSet(size);
for (int i = 0; i < size; i++) {
possibleItemIDs.add(preferencesFromUser.getItemID(i));
}
possibleItemIDs.remove(itemID);
List mostSimilar = mostSimilarItems(itemID, possibleItemIDs.iterator(),
neighborhoodSize, null);
long[] theNeighborhood = new long[mostSimilar.size() + 1];
theNeighborhood[0] = -1;
List usersRatedNeighborhood = Lists.newArrayList();
int nOffset = 0;
for (RecommendedItem rec : mostSimilar) {
theNeighborhood[nOffset++] = rec.getItemID();
}
if (!mostSimilar.isEmpty()) {
theNeighborhood[mostSimilar.size()] = itemID;
for (int i = 0; i < theNeighborhood.length; i++) {
PreferenceArray usersNeighborhood = dataModel.getPreferencesForItem(theNeighborhood[i]);
int size1 = usersRatedNeighborhood.isEmpty() ? usersNeighborhood.length() : usersRatedNeighborhood.size();
for (int j = 0; j < size1; j++) {
if (i == 0) {
usersRatedNeighborhood.add(usersNeighborhood.getUserID(j));
} else {
if (j >= usersRatedNeighborhood.size()) {
break;
}
long index = usersRatedNeighborhood.get(j);
if (!usersNeighborhood.hasPrefWithUserID(index) || index == theUserID) {
usersRatedNeighborhood.remove(index);
j--;
}
}
}
}
}
double[] weights = null;
if (!mostSimilar.isEmpty()) {
weights = getInterpolations(itemID, theNeighborhood, usersRatedNeighborhood);
}
int i = 0;
double preference = 0.0;
double totalSimilarity = 0.0;
for (long jitem : theNeighborhood) {
Float pref = dataModel.getPreferenceValue(theUserID, jitem);
if (pref != null) {
double weight = weights[i];
preference += pref * weight;
totalSimilarity += weight;
}
i++;
}
return totalSimilarity == 0.0 ? Float.NaN : (float) (preference / totalSimilarity);
}
}
測試程式:
public static void itemKNN(DataModel dataModel) throws TasteException {
ItemSimilarity itemSimilarity = RecommendFactory.itemSimilarity(RecommendFactory.SIMILARITY.EUCLIDEAN, dataModel);
RecommenderBuilder recommenderBuilder = RecommendFactory.itemKNNRecommender(itemSimilarity, new NonNegativeQuadraticOptimizer(), 10);
RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
LongPrimitiveIterator iter = dataModel.getUserIDs();
while (iter.hasNext()) {
long uid = iter.nextLong();
List list = recommenderBuilder.buildRecommender(dataModel).recommend(uid, RECOMMENDER_NUM);
RecommendFactory.showItems(uid, list, true);
}
}
程式輸出:
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:1.5
Recommender IR Evaluator: [Precision:0.5,Recall:1.0]
uid:1,(107,5.000000)(104,3.501168)(106,3.498198)
uid:2,(105,2.878995)(106,2.878086)(107,2.000000)
uid:3,(103,3.667444)(102,3.667161)(106,3.667019)
uid:4,(107,4.750247)(102,4.122755)(105,4.122709)
uid:5,(107,3.833621)
9. SVD推薦演算法
(TODO未完)
演算法API: org.apache.mahout.cf.taste.impl.recommender.svd.SVDRecommender
@Override
public float estimatePreference(long userID, long itemID) throws TasteException {
double[] userFeatures = factorization.getUserFeatures(userID);
double[] itemFeatures = factorization.getItemFeatures(itemID);
double estimate = 0;
for (int feature = 0; feature < userFeatures.length; feature++) {
estimate += userFeatures[feature] * itemFeatures[feature];
}
return (float) estimate;
}
測試程式:
public static void svd(DataModel dataModel) throws TasteException {
RecommenderBuilder recommenderBuilder = RecommendFactory.svdRecommender(new ALSWRFactorizer(dataModel, 10, 0.05, 10));
RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
LongPrimitiveIterator iter = dataModel.getUserIDs();
while (iter.hasNext()) {
long uid = iter.nextLong();
List list = recommenderBuilder.buildRecommender(dataModel).recommend(uid, RECOMMENDER_NUM);
RecommendFactory.showItems(uid, list, true);
}
}
程式輸出:
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:0.09990564982096355
Recommender IR Evaluator: [Precision:0.5,Recall:1.0]
uid:1,(104,4.032909)(105,3.390885)(107,1.858541)
uid:2,(105,3.761718)(106,2.951908)(107,1.561116)
uid:3,(103,5.593422)(102,2.458930)(106,-0.091259)
uid:4,(105,4.068329)(102,3.534025)(107,0.206257)
uid:5,(107,0.105169)
10. Tree Cluster-based 推薦演算法
這個演算法在mahout-0.8版本中,已經被@Deprecated。
(TODO未完)
演算法API: org.apache.mahout.cf.taste.impl.recommender.TreeClusteringRecommender
@Override
public float estimatePreference(long userID, long itemID) throws TasteException {
DataModel model = getDataModel();
Float actualPref = model.getPreferenceValue(userID, itemID);
if (actualPref != null) {
return actualPref;
}
buildClusters();
List topRecsForUser = topRecsByUserID.get(userID);
if (topRecsForUser != null) {
for (RecommendedItem item : topRecsForUser) {
if (itemID == item.getItemID()) {
return item.getValue();
}
}
}
// Hmm, we have no idea. The item is not in the user's cluster
return Float.NaN;
}
測試程式:
public static void treeCluster(DataModel dataModel) throws TasteException {
UserSimilarity userSimilarity = RecommendFactory.userSimilarity(RecommendFactory.SIMILARITY.LOGLIKELIHOOD, dataModel);
ClusterSimilarity clusterSimilarity = RecommendFactory.clusterSimilarity(RecommendFactory.SIMILARITY.FARTHEST_NEIGHBOR_CLUSTER, userSimilarity);
RecommenderBuilder recommenderBuilder = RecommendFactory.treeClusterRecommender(clusterSimilarity, 10);
RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
LongPrimitiveIterator iter = dataModel.getUserIDs();
while (iter.hasNext()) {
long uid = iter.nextLong();
List list = recommenderBuilder.buildRecommender(dataModel).recommend(uid, RECOMMENDER_NUM);
RecommendFactory.showItems(uid, list, true);
}
}
程式輸出:
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:NaN
Recommender IR Evaluator: [Precision:NaN,Recall:0.0]
11. Mahout推薦演算法總結
演算法及適用場景:
演算法評分的結果:
通過對上面幾種演算法的一平分比較:itemCF,itemKNN,SVD的Rrecision,Recall的評分值是最好的,並且itemCF和 SVD的AVERAGE_ABSOLUTE_DIFFERENCE是最低的,所以,從演算法的角度知道了,哪個演算法是更準確的或者會索引到更多的資料集。
另外的一些因素:
- 1. 這3個指標,並不能直接決定計算結果一定itemCF,SVD好
- 2. 各種演算法的引數我們並沒有調優
- 3. 資料量和資料分佈,是影響演算法的評分
程式原始碼下載