从源代码剖析Mahout推荐引擎

3). 构造近邻算法模型

   public enum NEIGHBORHOOD {
        NEAREST, THRESHOLD
    }

    public static UserNeighborhood userNeighborhood(NEIGHBORHOOD type, UserSimilarity s, DataModel m, double num) throws TasteException {
        switch (type) {
        case NEAREST:
            return new NearestNUserNeighborhood((int) num, s, m);
        case THRESHOLD:
        default:
            return new ThresholdUserNeighborhood(num, s, m);
        }
    }

4). 构造推荐算法模型

  public enum RECOMMENDER {
        USER, ITEM
    }

    public static RecommenderBuilder userRecommender(final UserSimilarity us, final UserNeighborhood un, boolean pref) throws TasteException {
        return pref ? new RecommenderBuilder() {
            @Override
            public Recommender buildRecommender(DataModel model) throws TasteException {
                return new GenericUserBasedRecommender(model, un, us);
            }
        } : new RecommenderBuilder() {
            @Override
            public Recommender buildRecommender(DataModel model) throws TasteException {
                return new GenericBooleanPrefUserBasedRecommender(model, un, us);
            }
        };
    }

    public static RecommenderBuilder itemRecommender(final ItemSimilarity is, boolean pref) throws TasteException {
        return pref ? new RecommenderBuilder() {
            @Override
            public Recommender buildRecommender(DataModel model) throws TasteException {
                return new GenericItemBasedRecommender(model, is);
            }
        } : new RecommenderBuilder() {
            @Override
            public Recommender buildRecommender(DataModel model) throws TasteException {
                return new GenericBooleanPrefItemBasedRecommender(model, is);
            }
        };
    }

    public static RecommenderBuilder slopeOneRecommender() throws TasteException {
        return new RecommenderBuilder() {
            @Override
            public Recommender buildRecommender(DataModel dataModel) throws TasteException {
                return new SlopeOneRecommender(dataModel);
            }

        };
    }

    public static RecommenderBuilder itemKNNRecommender(final ItemSimilarity is, final Optimizer op, final int n) throws TasteException {
        return new RecommenderBuilder() {
            @Override
            public Recommender buildRecommender(DataModel dataModel) throws TasteException {
                return new KnnItemBasedRecommender(dataModel, is, op, n);
            }
        };
    }

    public static RecommenderBuilder svdRecommender(final Factorizer factorizer) throws TasteException {
        return new RecommenderBuilder() {
            @Override
            public Recommender buildRecommender(DataModel dataModel) throws TasteException {
                return new SVDRecommender(dataModel, factorizer);
            }
        };
    }

    public static RecommenderBuilder treeClusterRecommender(final ClusterSimilarity cs, final int n) throws TasteException {
        return new RecommenderBuilder() {
            @Override
            public Recommender buildRecommender(DataModel dataModel) throws TasteException {
                return new TreeClusteringRecommender(dataModel, cs, n);
            }
        };
    }

5). 构造算法评估模型

  public enum EVALUATOR {
        AVERAGE_ABSOLUTE_DIFFERENCE, RMS
    }

    public static RecommenderEvaluator buildEvaluator(EVALUATOR type) {
        switch (type) {
        case RMS:
            return new RMSRecommenderEvaluator();
        case AVERAGE_ABSOLUTE_DIFFERENCE:
        default:
            return new AverageAbsoluteDifferenceRecommenderEvaluator();
        }
    }

    public static void evaluate(EVALUATOR type, RecommenderBuilder rb, DataModelBuilder mb, DataModel dm, double trainPt) throws TasteException {
        System.out.printf("%s Evaluater Score:%s\n", type.toString(), buildEvaluator(type).evaluate(rb, mb, dm, trainPt, 1.0));
    }

    public static void evaluate(RecommenderEvaluator re, RecommenderBuilder rb, DataModelBuilder mb, DataModel dm, double trainPt) throws TasteException {
        System.out.printf("Evaluater Score:%s\n", re.evaluate(rb, mb, dm, trainPt, 1.0));
    }

    /**
     * statsEvaluator
     */
    public static void statsEvaluator(RecommenderBuilder rb, DataModelBuilder mb, DataModel m, int topn) throws TasteException {
        RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
        IRStatistics stats = evaluator.evaluate(rb, mb, m, null, topn, GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0);
        // System.out.printf("Recommender IR Evaluator: %s\n", stats);
        System.out.printf("Recommender IR Evaluator: [Precision:%s,Recall:%s]\n", stats.getPrecision(), stats.getRecall());
    }

6). 推荐结果输出

     public static void showItems(long uid, List recommendations, boolean skip) {
        if (!skip || recommendations.size() > 0) {
            System.out.printf("uid:%s,", uid);
            for (RecommendedItem recommendation : recommendations) {
                System.out.printf("(%s,%f)", recommendation.getItemID(), recommendation.getValue());
            }
            System.out.println();
        }
    }

7). 完整源代码文件及使用样例：
https://github.com/bsspirit/maven_mahout_template/tree/mahout-0.8/src/main/java/org/conan/mymahout/recommendation/job

转载请注明出处：
http://blog.fens.me/mahout-recommend-engine/