ALS召回、LR、GBDT排序的实战，A/B Test【转载的哦】

【转】https://blog.csdn.net/haozi_rou/article/details/104888594

在生成ALS和LR模型以后，接下来就可以用在代码中了。

首先ALS，其实在数据已经存在数据库中了，只要从中取出来，去掉个逗号之类的就好

@Service
public class RecommendService {
    @Autowired
    private RecommendDOMapper recommendDOMapper;
    //找回数据，根据userid召回shopidList
    public List<Integer> recall(Integer userId){
        RecommendDO recommendDO = recommendDOMapper.selectByPrimaryKey(userId);
        if (recommendDO == null){
            recommendDO = recommendDOMapper.selectByPrimaryKey(99999);
        }
        String[] shopIdArr = recommendDO.getRecommend().split(",");
        List<Integer> shopIdList = new ArrayList<>();
        for (int i = 0 ; i < shopIdArr.length ; i ++){
            shopIdList.add(Integer.valueOf(shopIdArr[i]));
        }
        return shopIdList;
    }
}

　　

对于LR：

@Service
public class RecommendSortService {
    private SparkSession spark;
    private LogisticRegressionModel lrModel;
    @PostConstruct
    public void init(){
        //初始化spark运行环境
        spark = SparkSession.builder()
                .master("local")
                .appName("DianpingApp")
                .getOrCreate();
        lrModel = LogisticRegressionModel.load("file:///F:/mouseSpace/project/background/lr/lrmodel");
    }
    public List<Integer> sort(List<Integer> shopIdList , Integer userId){
        //需要根据lrmodel所需要的11维的x生成特征，然后调用预测方法
        List<ShopSortModel> list = new ArrayList<>();
        for (Integer shopId : shopIdList){
            //造的假数据
            Vector v = Vectors.dense(1,0,0,0,0,1,0.6,0,0,1,0);
            Vector result = lrModel.predictProbability(v);
            double[] arr = result.toArray();
            double score = arr[1];
//            lrModel.predict(v);       如果用这个，就是返回1或者0
            ShopSortModel shopSortModel = new ShopSortModel();
            shopSortModel.setShopId(shopId);
            shopSortModel.setScore(score);
            list.add(shopSortModel);
        }
        list.sort(new Comparator<ShopSortModel>() {
            @Override
            public int compare(ShopSortModel o1, ShopSortModel o2) {
                if (o1.getScore() < o2.getScore()){
                    return -1;
                }else if (o1.getScore() > o2.getScore()){
                    return 1;
                }else {
                    return 0;
                }
            }
        });
        return list.stream().map(shopSortModel -> shopSortModel.getShopId()).collect(Collectors.toList());
    }
}

 

代码中自己造了一个数据，所以结果会有些偏差。

对于GBDT

跟lr算法非常像

public class GBDTRecommendSortService {
    private SparkSession spark;
    private GBTClassificationModel gbtClassificationModel;
    @PostConstruct
    public void init(){
        //初始化spark运行环境
        spark = SparkSession.builder()
                .master("local")
                .appName("DianpingApp")
                .getOrCreate();
        gbtClassificationModel = GBTClassificationModel.load("file:///F:/mouseSpace/project/background/lr/gbdtmodel");
    }
    public List<Integer> sort(List<Integer> shopIdList , Integer userId){
        //需要根据lrmodel所需要的11维的x生成特征，然后调用预测方法
        List<ShopSortModel> list = new ArrayList<>();
        for (Integer shopId : shopIdList){
            //造的假数据
            Vector v = Vectors.dense(1,0,0,0,0,1,0.6,0,0,1,0);
            Vector result = gbtClassificationModel.predictProbability(v);
            double[] arr = result.toArray();
            double score = arr[1];
//            lrModel.predict(v);       如果用这个，就是返回1或者0
            ShopSortModel shopSortModel = new ShopSortModel();
            shopSortModel.setShopId(shopId);
            shopSortModel.setScore(score);
            list.add(shopSortModel);
        }
        list.sort(new Comparator<ShopSortModel>() {
            @Override
            public int compare(ShopSortModel o1, ShopSortModel o2) {
                if (o1.getScore() < o2.getScore()){
                    return -1;
                }else if (o1.getScore() > o2.getScore()){
                    return 1;
                }else {
                    return 0;
                }
            }
        });
        return list.stream().map(shopSortModel -> shopSortModel.getShopId()).collect(Collectors.toList());
    }
}

　　

A/B Test
它可以帮助我们决策算法的好坏，提供更多的真实依据的手段。

在真实场景中，假如现有的是LR算法，那么我现在马上在线上换成GBDT，当然是有很大风险的，那么AB TEST就出现了，假如有10条数据，我可以分5条用lr算法，5条用GBDT算法，然后将两个依次穿插，形成一个结果集发给前端，然后通过记录点击率来验证哪种算法更好。