这是在排序时拿到的所有数据
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362579, \"p_price_online\": 9800, \"p_price_origin\": 19800, \"p_create_time\": \"12:19:11\", \"create_date\": \"2018-08-21\", \"p_gong_yn\": 0, \"p_is_vip\": 1, \"p_is_top\": 0, \"p_on_sale_yn\": 0, \"p_special_yn\": 0, \"p_micro_yn\": 0, \"p_operation_yn\": 0, \"p_inject_yn\": 0, \"p_laser_yn\": 0, \"p_hospital_id\": 117, \"p_product_doctor\": [141575], \"p_discount_rate\": 0.494949494949495, \"p_order_count\": 2, \"p_gmv_count\": 19600, \"p_order_rate\": 0, \"p_collect_rate\": 0, \"p_click_rate\": 0, \"ctr_30\": 0.03853992959051325, \"ctr_7\": 0.03150242326332795, \"ctr_3\": 0.02650602409638554, \"items\": [40031, 20107, 20195, 20029, 117, 592, 102, 464, 20123, 590, 40006, 722, 40062, 10019, 736, 680, 735, 20110, 194, 153, 232, 20120, 652, 28717, 853, 37, 672, 40049, 70004, 463, 613, 40030, 20154, 141575, 661, 532], \"item_ctrs\": [0.019230769230769232, 0.047619047619047616, 0.017467248908296942, 0.03125, 0.0759493670886076, 0.10526315789473684, 0.08333333333333333, 0.21052631578947367, 0.04878048780487805, 0.25, 0.029574861367837338, 0.038461538461538464, 0.1111111111111111, 0.06925207756232687, 0.2, 0.043010752688172046, 0.032, 0.041666666666666664, 0.09290953545232274, 0.06666666666666667, 0.03125, 0.03571428571428571, 0.019230769230769232, 0.10810810810810811, 0.015873015873015872, 0.21428571428571427, 0.030303030303030304, 0.03409090909090909, 0.043478260869565216, 0.045454545454545456, 0.08333333333333333, 0.022727272727272728, 0.05084745762711865, 0.1111111111111111, 0.0392156862745098, 0.039285714285714285], \"menu2s\": [20025, 20123, 20110, 20107, 20195, 20154, 20029, 20014], \"menu2_ctrs\": [0.08333333333333333, 0.25, 0.21052631578947367, 0.25, 0.07142857142857142, 0.08082191780821918, 0.03125, 0.21428571428571427]}","rank_score_model_map":{"XGB_PRODUCT_CTR_GHJ1":0.025919904932379723},"key":"31_362579_152719346979591396"}
我们需要解析出所需特征，然后进行模型训练或评分。
一、Context特征
①timeStamp转成四个特征：
c_day_of_week_：5（周五）
c_hour_of_day_：13（13点）
c_fen10_of_hour_：2（13:24分得到值为2）当前小时过了多少个10分钟
c_fen10_of_day_：67（今天过了多少个10分钟）
②经纬度：经纬度都保留一位小数
c_latitude_:39.2
c_longitude_:117.1
③app版本、手机类型、来源页、
c_app_version_:7380
c_sys_:1
c_from_positon_:2
④搜索词：
oqpl_keyword_双眼皮:1.0
二、User特征
①性别：男（u_final_gender_1:1.0） 女（u_final_gender_0:1.0）未知（u_final_gender_-1:1.0）
说明：推测用户性别有多用途径。例如：保险性别 > 魔镜识别性别 > 注册性别。性别特征值都是1，只不过key不同。
②年龄、订单数、退款数、近30天访问天数、上次活跃天数、日记数、点赞数、发帖数、停留时长、支付金额
处理：如果是0就直接寸0，如果非0就+1取log，然后保留一位或两位小数。
u_log_total_order_cnt_:2.9
u_log_active_day:1.3
u_log_post_cnt:4.3
注意：连续值特征的key都是相同的，只不过value不同。
③是否是常住地
三、Item特征
①离散数值型和各种率
i_is_vip:1.0
i_click_rate:0.4
直接保留一位小数。
②连续数值型特征
i_price_online_ :9.2
i_gmv_cnt:19.9
加1取log，保留一位小数
③近3，7，30天ctr
i_ctr_3_:0.026506023
i_ctr_7_:0.031502422
④click_money、arpu
p_log_arpu_:23.92
p_log_arpu_:3.2
这些既存原始值，也保存log后的值。
⑤机构id：
p_hospital_id_117:1
独热处理：例如这个商品关联了hospital_id是117，那就p_hospital_id_117:1
①竞价金额：
i_bid_:44.3
②粗排召回分
i_pre_socre:70023.2
③文本相关性
i_es_title_score:223.3
i_es_user_name_score:42.3
④商品类型
i_product_weight:2.0（之前product_weight分成三段（1,2~100,101~10000），现在转换成1，2，3三种类型）
⑤商品类目：
例如这个商品的一级类目是[1003,1007]，二级类目是[2013]那就需要增加三个特征：
i_product_menu1_1003:1.0
i_product_menu1_1007:1.0
i_product_menu2_2013:1.0
⑥商品城市：
同时增加是否同城、是否同省
i_city_9:1.0
i_province_id_9:1.0
i_same_city:0.0
i_same_province:0.0
⑦商品到用户距离：取log
⑧历史点击：
对应三个特征：
命中：his_click:1、his_click_weight:0.5、his_click_weight_1:1
没命中：his_click:1、his_click_weight:0.5、his_click_weight_1:1
搜索点击列表、安心购列表、加车列表。因为这类都是商品类点击，所以我们可以把它们给合并成一个列表，我们需要给他们赋予不同的权重。例如我们比较看重用户的加车列表，我们可以给赋予1，其他赋值0.5。然后合并列表。如果没有点击列表就没有这三个特征。
说明：
①特征分为三类：context特征，item特征、user特征。为了方便区分：每一类都会分别加上c、i、u的前缀
②开始的字符串，一部分数据是由uid从redis中取user特征、由pid取出商品相关的item特征，拼接而成的。
③经纬度的特征处理方式：也可以使用GEOHash，把经纬度转换成地球上的区域。
