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用Mahout构建职位推荐引擎【一起学Mahout】

2024-08-01 02:20:55   223人阅读

阅读导读：
1.如何设计职位推荐引擎的指标？
2.简述职位推荐引擎所需要的系统架构？
3.如何对推荐结果进行人工比较？
4.职位推荐引擎中什么情况的数据最好做排除？



1. Mahout推荐系统框架概述


Mahout框架包含了一套完整的推荐系统引擎，标准化的数据结构，多样的算法实现，简单的开发流程。Mahout推荐的推荐系统引擎是模块化的，分为5个主要部分组成：数据模型，相似度算法，近邻算法，推荐算法，算法评分器。

更详细的介绍，请参考文章：从源代码剖析Mahout推荐引擎

2. 需求分析：职位推荐引擎指标设计


下面我们将从一个公司案例出发来全面的解释，如何进行职位推荐引擎指标设计。

案例介绍：
互联网某职业社交网站，主要产品包括 个人简历展示页，人脉圈，微博及分享链接，职位发布，职位申请，教育培训等。

用户在完成注册后，需要完善自己的个人信息，包括教育背景，工作经历，项目经历，技能专长等等信息。然后，你要告诉网站，你是否想找工作！！当你选择“是”（求职中），网站会从数据库中为你推荐你可能感兴趣的职位。

通过简短的描述，我们可以粗略地看出，这家职业社交网站的定位和主营业务。核心点有2个：
  • 用户：尽可能多的保存有效完整的用户资料
  • 服务：帮助用户找到工作，帮助猎头和企业找到员工

因此，职位推荐引擎 将成为这个网站的核心功能。
KPI指标设计
  • 通过推荐带来的职位浏览量: 职位网页的PV
  • 通过推荐带来的职位申请量: 职位网页的有效转化

3. 算法模型：推荐算法


2个测试数据集：
  • pv.csv: 职位被浏览的信息,包括用户ID，职位ID
  • job.csv: 职位基本信息,包括职位ID，发布时间，工资标准

1). pv.csv

  • 2列数据：用户ID，职位ID(userid,jobid)
  • 浏览记录:2500条
  • 用户数:1000个，用户ID:1-1000
  • 职位数:200个，职位ID：1-200
部分数据：
1,11
2,136
2,187
3,165
3,1
3,24
4,8
4,199
5,32
5,100
6,14
7,59
7,147
8,92
9,165
9,80
9,171
10,45
10,31
10,1
10,152

2). job.csv

  • 3列数据：职位ID，发布时间，工资标准(jobid,create_date,salary)
  • 职位数:200个，职位ID：1-200

部分数据：
1,2013-01-24,5600
2,2011-03-02,5400
3,2011-03-14,8100
4,2012-10-05,2200
5,2011-09-03,14100
6,2011-03-05,6500
7,2012-06-06,37000
8,2013-02-18,5500
9,2010-07-05,7500
10,2010-01-23,6700
11,2011-09-19,5200
12,2010-01-19,29700
13,2013-09-28,6000
14,2013-10-23,3300
15,2010-10-09,2700
16,2010-07-14,5100
17,2010-05-13,29000
18,2010-01-16,21800
19,2013-05-23,5700
20,2011-04-24,5900
为了完成KPI的指标，我们把问题用“技术”语言转化一下：我们需要让职位的推荐结果更准确，从而增加用户的点击。
  • 1. 组合使用推荐算法，选出“评估推荐器”验证得分较高的算法
  • 2. 人工验证推荐结果
  • 3. 职位有时效性，推荐的结果应该是发布半年内的职位
  • 4. 工资的标准，应不低于用户浏览职位工资的平均值的80%

我们选择UserCF,ItemCF,SlopeOne的 3种推荐算法，进行7种组合的测试。
  • userCF1: LogLikelihoodSimilarity + NearestNUserNeighborhood + GenericBooleanPrefUserBasedRecommender
  • userCF2: CityBlockSimilarity+ NearestNUserNeighborhood + GenericBooleanPrefUserBasedRecommender
  • userCF3: UserTanimoto + NearestNUserNeighborhood + GenericBooleanPrefUserBasedRecommender
  • itemCF1: LogLikelihoodSimilarity + GenericBooleanPrefItemBasedRecommender
  • itemCF2: CityBlockSimilarity+ GenericBooleanPrefItemBasedRecommender
  • itemCF3: ItemTanimoto + GenericBooleanPrefItemBasedRecommender
  • slopeOne：SlopeOneRecommender

关于的推荐算法的详细介绍，请参考文章：Mahout推荐算法API详解

关于算法的组合的详细介绍，请参考文章：从源代码剖析Mahout推荐引擎

4. 架构设计：职位推荐引擎系统架构


上图中，左边是Application业务系统，右边是Mahout，下边是Hadoop集群。
  • 1. 当数据量不太大时，并且算法复杂，直接选择用Mahout读取CSV或者Database数据，在单机内存中进行计算。Mahout是多线程的应用，会并行使用单机所有系统资源。
  • 2. 当数据量很大时，选择并行化算法(ItemCF)，先业务系统的数据导入到Hadoop的HDFS中，然后用Mahout访问HDFS实现算法，这时算法的性能与整个Hadoop集群有关。
  • 3. 计算后的结果，保存到数据库中，方便查询

5. 程序开发：基于Mahout的推荐算法实现


开发环境mahout版本为0.8。 ，请参考文章：用Maven构建Mahout项目

新建Java类：
  • RecommenderEvaluator.java, 选出“评估推荐器”验证得分较高的算法
  • RecommenderResult.java, 对指定数量的结果人工比较
  • RecommenderFilterOutdateResult.java，排除过期职位
  • RecommenderFilterSalaryResult.java，排除工资过低的职位

1). RecommenderEvaluator.java, 选出“评估推荐器”验证得分较高的算

源代码：
public class RecommenderEvaluator {

    final static int NEIGHBORHOOD_NUM = 2;
    final static int RECOMMENDER_NUM = 3;

    public static void main(String[] args) throws TasteException, IOException {
        String file = "datafile/job/pv.csv";
        DataModel dataModel = RecommendFactory.buildDataModelNoPref(file);
        userLoglikelihood(dataModel);
        userCityBlock(dataModel);
        userTanimoto(dataModel);
        itemLoglikelihood(dataModel);
        itemCityBlock(dataModel);
        itemTanimoto(dataModel);
        slopeOne(dataModel);
    }

    public static RecommenderBuilder userLoglikelihood(DataModel dataModel) throws TasteException, IOException {
        System.out.println("userLoglikelihood");
        UserSimilarity userSimilarity = RecommendFactory.userSimilarity(RecommendFactory.SIMILARITY.LOGLIKELIHOOD, dataModel);
        UserNeighborhood userNeighborhood = RecommendFactory.userNeighborhood(RecommendFactory.NEIGHBORHOOD.NEAREST, userSimilarity, dataModel, NEIGHBORHOOD_NUM);
        RecommenderBuilder recommenderBuilder = RecommendFactory.userRecommender(userSimilarity, userNeighborhood, false);

        RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
        RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
        return recommenderBuilder;
    }

    public static RecommenderBuilder userCityBlock(DataModel dataModel) throws TasteException, IOException {
        System.out.println("userCityBlock");
        UserSimilarity userSimilarity = RecommendFactory.userSimilarity(RecommendFactory.SIMILARITY.CITYBLOCK, dataModel);
        UserNeighborhood userNeighborhood = RecommendFactory.userNeighborhood(RecommendFactory.NEIGHBORHOOD.NEAREST, userSimilarity, dataModel, NEIGHBORHOOD_NUM);
        RecommenderBuilder recommenderBuilder = RecommendFactory.userRecommender(userSimilarity, userNeighborhood, false);

        RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
        RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
        return recommenderBuilder;
    }

    public static RecommenderBuilder userTanimoto(DataModel dataModel) throws TasteException, IOException {
        System.out.println("userTanimoto");
        UserSimilarity userSimilarity = RecommendFactory.userSimilarity(RecommendFactory.SIMILARITY.TANIMOTO, dataModel);
        UserNeighborhood userNeighborhood = RecommendFactory.userNeighborhood(RecommendFactory.NEIGHBORHOOD.NEAREST, userSimilarity, dataModel, NEIGHBORHOOD_NUM);
        RecommenderBuilder recommenderBuilder = RecommendFactory.userRecommender(userSimilarity, userNeighborhood, false);

        RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
        RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
        return recommenderBuilder;
    }

    public static RecommenderBuilder itemLoglikelihood(DataModel dataModel) throws TasteException, IOException {
        System.out.println("itemLoglikelihood");
        ItemSimilarity itemSimilarity = RecommendFactory.itemSimilarity(RecommendFactory.SIMILARITY.LOGLIKELIHOOD, dataModel);
        RecommenderBuilder recommenderBuilder = RecommendFactory.itemRecommender(itemSimilarity, false);

        RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
        RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
        return recommenderBuilder;
    }

    public static RecommenderBuilder itemCityBlock(DataModel dataModel) throws TasteException, IOException {
        System.out.println("itemCityBlock");
        ItemSimilarity itemSimilarity = RecommendFactory.itemSimilarity(RecommendFactory.SIMILARITY.CITYBLOCK, dataModel);
        RecommenderBuilder recommenderBuilder = RecommendFactory.itemRecommender(itemSimilarity, false);

        RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
        RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
        return recommenderBuilder;
    }

    public static RecommenderBuilder itemTanimoto(DataModel dataModel) throws TasteException, IOException {
        System.out.println("itemTanimoto");
        ItemSimilarity itemSimilarity = RecommendFactory.itemSimilarity(RecommendFactory.SIMILARITY.TANIMOTO, dataModel);
        RecommenderBuilder recommenderBuilder = RecommendFactory.itemRecommender(itemSimilarity, false);

        RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
        RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
        return recommenderBuilder;
    }

    public static RecommenderBuilder slopeOne(DataModel dataModel) throws TasteException, IOException {
        System.out.println("slopeOne");
        RecommenderBuilder recommenderBuilder = RecommendFactory.slopeOneRecommender();

        RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
        RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
        return recommenderBuilder;
    }

    public static RecommenderBuilder knnLoglikelihood(DataModel dataModel) throws TasteException, IOException {
        System.out.println("knnLoglikelihood");
        ItemSimilarity itemSimilarity = RecommendFactory.itemSimilarity(RecommendFactory.SIMILARITY.LOGLIKELIHOOD, 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);

        return recommenderBuilder;
    }

    public static RecommenderBuilder knnTanimoto(DataModel dataModel) throws TasteException, IOException {
        System.out.println("knnTanimoto");
        ItemSimilarity itemSimilarity = RecommendFactory.itemSimilarity(RecommendFactory.SIMILARITY.TANIMOTO, 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);

        return recommenderBuilder;
    }

    public static RecommenderBuilder knnCityBlock(DataModel dataModel) throws TasteException, IOException {
        System.out.println("knnCityBlock");
        ItemSimilarity itemSimilarity = RecommendFactory.itemSimilarity(RecommendFactory.SIMILARITY.CITYBLOCK, 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);

        return recommenderBuilder;
    }

    public static RecommenderBuilder svd(DataModel dataModel) throws TasteException {
        System.out.println("svd");
        RecommenderBuilder recommenderBuilder = RecommendFactory.svdRecommender(new ALSWRFactorizer(dataModel, 5, 0.05, 10));

        RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
        RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);

        return recommenderBuilder;
    }

    public static RecommenderBuilder treeClusterLoglikelihood(DataModel dataModel) throws TasteException {
        System.out.println("treeClusterLoglikelihood");
        UserSimilarity userSimilarity = RecommendFactory.userSimilarity(RecommendFactory.SIMILARITY.LOGLIKELIHOOD, dataModel);
        ClusterSimilarity clusterSimilarity = RecommendFactory.clusterSimilarity(RecommendFactory.SIMILARITY.FARTHEST_NEIGHBOR_CLUSTER, userSimilarity);
        RecommenderBuilder recommenderBuilder = RecommendFactory.treeClusterRecommender(clusterSimilarity, 3);

        RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
        RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);

        return recommenderBuilder;
    }
}
运行结果，控制台输出：
userLoglikelihood
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:0.2741487771272658
Recommender IR Evaluator: [Precision:0.6424242424242422,Recall:0.4098360655737705]
userCityBlock
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:0.575306732961736
Recommender IR Evaluator: [Precision:0.919580419580419,Recall:0.4371584699453552]
userTanimoto
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:0.5546485136181523
Recommender IR Evaluator: [Precision:0.6625766871165644,Recall:0.41803278688524603]
itemLoglikelihood
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:0.5398332608612343
Recommender IR Evaluator: [Precision:0.26229508196721296,Recall:0.26229508196721296]
itemCityBlock
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:0.9251437840891661
Recommender IR Evaluator: [Precision:0.02185792349726776,Recall:0.02185792349726776]
itemTanimoto
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:0.9176432856689655
Recommender IR Evaluator: [Precision:0.26229508196721296,Recall:0.26229508196721296]
slopeOne
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:0.0
Recommender IR Evaluator: [Precision:0.01912568306010929,Recall:0.01912568306010929]
可视化“评估推荐器”输出：


UserCityBlock算法评估的结果是最好的，基于UserCF的算法比ItemCF都要好，SlopeOne算法几乎没有得分。

2). RecommenderResult.java, 对指定数量的结果人工比较

为得到差异化结果，我们分别取UserCityBlock,itemLoglikelihood，对推荐结果人工比较。

源代码：
public class RecommenderResult {

    final static int NEIGHBORHOOD_NUM = 2;
    final static int RECOMMENDER_NUM = 3;

    public static void main(String[] args) throws TasteException, IOException {
        String file = "datafile/job/pv.csv";
        DataModel dataModel = RecommendFactory.buildDataModelNoPref(file);
        RecommenderBuilder rb1 = RecommenderEvaluator.userCityBlock(dataModel);
        RecommenderBuilder rb2 = RecommenderEvaluator.itemLoglikelihood(dataModel);

        LongPrimitiveIterator iter = dataModel.getUserIDs();
        while (iter.hasNext()) {
            long uid = iter.nextLong();
            System.out.print("userCityBlock    =>");
            result(uid, rb1, dataModel);
            System.out.print("itemLoglikelihood=>");
            result(uid, rb2, dataModel);
        }
    }

    public static void result(long uid, RecommenderBuilder recommenderBuilder, DataModel dataModel) throws TasteException {
        List list = recommenderBuilder.buildRecommender(dataModel).recommend(uid, RECOMMENDER_NUM);
        RecommendFactory.showItems(uid, list, false);
    }

}
控制台输出：只截取部分结果
...
userCityBlock    =>uid:968,(61,0.333333)
itemLoglikelihood=>uid:968,(121,1.429362)(153,1.239939)(198,1.207726)
userCityBlock    =>uid:969,
itemLoglikelihood=>uid:969,(75,1.326499)(30,0.873100)(85,0.763344)
userCityBlock    =>uid:970,
itemLoglikelihood=>uid:970,(13,0.748417)(156,0.748417)(122,0.748417)
userCityBlock    =>uid:971,
itemLoglikelihood=>uid:971,(38,2.060951)(104,1.951208)(83,1.941735)
userCityBlock    =>uid:972,
itemLoglikelihood=>uid:972,(131,1.378395)(4,1.349386)(87,0.881816)
userCityBlock    =>uid:973,
itemLoglikelihood=>uid:973,(196,1.432040)(140,1.398066)(130,1.380335)
userCityBlock    =>uid:974,(19,0.200000)
itemLoglikelihood=>uid:974,(145,1.994049)(121,1.794289)(98,1.738027)
...
我们查看uid=974的用户推荐信息：
搜索pv.csv：
> pv[which(pv$userid==974),]
     userid jobid
2426    974   106
2427    974   173
2428    974    82
2429    974   188
2430    974    78
搜索job.csv:
> job[job$jobid %in% c(145,121,98,19),]
    jobid create_date salary
19     19  2013-05-23   5700
98     98  2010-01-15   2900
121   121  2010-06-19   5300
145   145  2013-08-02   6800
上面两种算法，推荐的结果都是2010年的职位，这些结果并不是太好，接下来我们要排除过期职位，只保留2013年的职位。

3).RecommenderFilterOutdateResult.java，排除过期职位

源代码：
public class RecommenderFilterOutdateResult {

    final static int NEIGHBORHOOD_NUM = 2;
    final static int RECOMMENDER_NUM = 3;

    public static void main(String[] args) throws TasteException, IOException {
        String file = "datafile/job/pv.csv";
        DataModel dataModel = RecommendFactory.buildDataModelNoPref(file);
        RecommenderBuilder rb1 = RecommenderEvaluator.userCityBlock(dataModel);
        RecommenderBuilder rb2 = RecommenderEvaluator.itemLoglikelihood(dataModel);

        LongPrimitiveIterator iter = dataModel.getUserIDs();
        while (iter.hasNext()) {
            long uid = iter.nextLong();
            System.out.print("userCityBlock    =>");
            filterOutdate(uid, rb1, dataModel);
            System.out.print("itemLoglikelihood=>");
            filterOutdate(uid, rb2, dataModel);
        }
    }

    public static void filterOutdate(long uid, RecommenderBuilder recommenderBuilder, DataModel dataModel) throws TasteException, IOException {
        Set jobids = getOutdateJobID("datafile/job/job.csv");
        IDRescorer rescorer = new JobRescorer(jobids);
        List list = recommenderBuilder.buildRecommender(dataModel).recommend(uid, RECOMMENDER_NUM, rescorer);
        RecommendFactory.showItems(uid, list, true);
    }

    public static Set getOutdateJobID(String file) throws IOException {
        BufferedReader br = new BufferedReader(new FileReader(new File(file)));
        Set jobids = new HashSet();
        String s = null;
        while ((s = br.readLine()) != null) {
            String[] cols = s.split(",");
            SimpleDateFormat df = new SimpleDateFormat("yyyy-MM-dd");
            Date date = null;
            try {
                date = df.parse(cols[1]);
                if (date.getTime() < df.parse("2013-01-01").getTime()) {
                    jobids.add(Long.parseLong(cols[0]));
                }
            } catch (ParseException e) {
                e.printStackTrace();
            }

        }
        br.close();
        return jobids;
    }

}

class JobRescorer implements IDRescorer {
    final private Set jobids;

    public JobRescorer(Set jobs) {
        this.jobids = jobs;
    }

    @Override
    public double rescore(long id, double originalScore) {
        return isFiltered(id) ? Double.NaN : originalScore;
    }

    @Override
    public boolean isFiltered(long id) {
        return jobids.contains(id);
    }
}
控制台输出：只截取部分结果
...
itemLoglikelihood=>uid:965,(200,0.829600)(122,0.748417)(170,0.736340)
userCityBlock    =>uid:966,(114,0.250000)
itemLoglikelihood=>uid:966,(114,1.516898)(101,0.864536)(99,0.856057)
userCityBlock    =>uid:967,
itemLoglikelihood=>uid:967,(105,0.873100)(114,0.725016)(168,0.707119)
userCityBlock    =>uid:968,
itemLoglikelihood=>uid:968,(174,0.735004)(39,0.696716)(185,0.696171)
userCityBlock    =>uid:969,
itemLoglikelihood=>uid:969,(197,0.723203)(81,0.710230)(167,0.668358)
userCityBlock    =>uid:970,
itemLoglikelihood=>uid:970,(13,0.748417)(122,0.748417)(28,0.736340)
userCityBlock    =>uid:971,
itemLoglikelihood=>uid:971,(28,1.540753)(174,1.511881)(39,1.435575)
userCityBlock    =>uid:972,
itemLoglikelihood=>uid:972,(14,0.800605)(60,0.794088)(163,0.710230)
userCityBlock    =>uid:973,
itemLoglikelihood=>uid:973,(56,0.795529)(13,0.712680)(120,0.701026)
userCityBlock    =>uid:974,(19,0.200000)
itemLoglikelihood=>uid:974,(145,1.994049)(89,1.578694)(19,1.435193)
...
我们查看uid=994的用户推荐信息：
搜索pv.csv：
> pv[which(pv$userid==974),]
     userid jobid
2426    974   106
2427    974   173
2428    974    82
2429    974   188
2430    974    78
搜索job.csv:
> job[job$jobid %in% c(19,145,89),]
    jobid create_date salary
19     19  2013-05-23   5700
89     89  2013-06-15   8400
145   145  2013-08-02   6800
排除过期的职位比较，我们发现userCityBlock结果都是19，itemLoglikelihood的第2，3的结果被替换为了得分更低的89和19。

4).RecommenderFilterSalaryResult.java，排除工资过低的职位

我们查看uid=994的用户，浏览过的职位。
> job[job$jobid %in% c(106,173,82,188,78),]
    jobid create_date salary
78     78  2012-01-29   6800
82     82  2010-07-05   7500
106   106  2011-04-25   5200
173   173  2013-09-13   5200
188   188  2010-07-14   6000
平均工资为=6140，我们觉得用户的浏览职位的行为，一般不会看比自己现在工资低的职位，因此设计算法，排除工资低于平均工资80%的职位，即排除工资小于4912的推荐职位(6140*0.8=4912)

大家可以参考上文中RecommenderFilterOutdateResult.java,自行实现。

这样，我们就完成用Mahout构建职位推荐引擎的算法。如果没有Mahout，我们自己写这个算法引擎估计还要花个小半年的时间，善加利用开源技术会帮助我们飞一样的成长！！




用Mahout构建职位推荐引擎【一起学Mahout】


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