请求参数
请求参数为JSON格式，参数项如下：
参数项 | 说明 |
request_id | string, 请求ID |
scene | string, 场景名，可选 |
algo | string, 算法名 |
user_id | string, 用户ID |
items | string数组, 候选arm集合 |
user_feature | dict, 用户特征 |
item_features | dict数组，Arm特征，可选 |
limit | 返回结果数量 |
返回结果
模型打分服务的返回结果也是json格式，如下：
{
"output": [
{
"id": "123",
"score": "0.8"
},
{
"id": "223",
"score": "0.7"
},
{
"id": "323",
"score": "0.6"
}
]
}配置参数
配置项 | 说明 |
HologresDSN | dict, hologres的链接字符串 |
SceneConf | dict, 场景配置，场景算法名+算法详细配置 |
SceneConf.${algo}.AlgoType | 算法类型：random，disjoint, hybrid |
SceneConf.${algo}.ArmFeatureDao | Arm原始特征表配置，打分服务从该表加载原始Arm特征 |
SceneConf.${algo}.LinUCBConf | LinUCB算法参数配置 |
SceneConf.${algo}.LinUCBConf.Alpha | 用来平衡Exploration&Exploitation的参数，值越大越偏向于Exploration |
SceneConf.${algo}.LinUCBConf.GlobalModelParamId | Hybrid算法的共享特征参数在 ModelTable 中的Key |
SceneConf.${algo}.LinUCBConf.HologresName | Hologres链接字符串字典的key |
SceneConf.${algo}.LinUCBConf.ModelTable | 模型参数Hologres表 |
SceneConf.${algo}.LinUCBConf.FeatureTable | FG之后的特征向量Hologres表 |
SceneConf.${algo}.LinUCBConf.FeatureTablePKey | FG之后的特征向量Hologres表的主键 |
SceneConf.${algo}.LinUCBConf.ArmIdKey | ArmId在Hologres表的字段名 |
SceneConf.${algo}.LinUCBConf.CacheMaxSize | 缓存在本地内存的模型参数最大数量 |
SceneConf.${algo}.LinUCBConf.CacheTimeInSeconds | 本地缓存的有效时间, 单位：秒 |
SceneConf.${algo}.LinUCBConf.RequestArmNumPerTime | 每次请求最多同步多少个模型参数 |
SceneConf.${algo}.LinUCBConf.RequestArmInterval | 模型参数同步的时间间隔 |
SceneConf.${algo}.LinUCBConf.Parallelism | 模型预测的并行度 |
SceneConf.${algo}.FeatureConf | FeatureGenerator特征配置 |
备注：
AlgoType: 算法类型
random表示随机从输入arm candidates中返回指定数量的arm；
disjoint表示不共享权重参数的linucb算法；
hybrid表示部分共享权重参数的linucb算法
完整配置文件示例
{
"HologresDSN": {
"vv_rec": "postgres://${asscessId}:${asscessKey}@hgpostcn-cn-xxxx-cn-beijing-vpc.hologres.aliyuncs.com:80/${db_name}?sslmode=disable&connect_timeout=10"
},
"AlgoConf": {
"default": {
"AlgoType": "random"
},
"cold_start": {
"ArmFeatureDao": [
{
"HologresName": "vv_rec",
"HologresTableName": "sv_rec.sv_dropoutnet_embedding",
"FeatureKey": "svid",
"SelectFields": "svid,embedding"
}
],
"AlgoType": "hybrid",
"CompressFeature": false,
"LinUCBConf": {
"Alpha": 1.0,
"ArmIdKey": "arm_id",
"HologresName": "vv_rec",
"ModelTable": "sv_rec.contextual_bandit_models_v2",
"FeatureTable": "sv_rec.contextual_bandit_features_v2",
"FeatureTablePKey": "user_id,arm_id",
"CacheTimeInSeconds": 86400,
"CacheMaxSize": 100000,
"RequestArmNumPerTime": 100,
"RequestArmInterval": 5,
"Parallelism": 8
},
"FeatureConf": [
{
"feature_type": "raw_feature",
"expression": "user:ColdStartVideoVectorRecall_embedding",
"value_dimension": 64,
"separator": ","
},
{
"feature_type": "raw_feature",
"expression": "arm:embedding",
"value_dimension": 64,
"share_weight": true
},
{
"feature_type": "raw_feature",
"expression": "arm:recall_score",
"share_weight": true
}
]
},
"linucb": {
"ArmFeatureDao": [
{
"HologresName": "vv_rec",
"HologresTableName": "vvarticle.smart_video",
"FeatureKey": "smartVideoId",
"SelectFields": "*"
}
],
"AlgoType": "disjoint",
"CompressFeature": true,
"LinUCBConf": {
"Alpha": 1.0,
"ArmIdKey": "arm_id",
"HologresName": "vv_rec",
"ModelTable": "sv_rec.contextual_bandit_models",
"FeatureTable": "sv_rec.contextual_bandit_features",
"FeatureTablePKey": "user_id,arm_id",
"CacheTimeInSeconds": 86400,
"CacheMaxSize": 100000,
"RequestArmNumPerTime": 100,
"RequestArmInterval": 5,
"Parallelism": 8
},
"FeatureConf": [
{
"feature_type": "binary_feature",
"expression": "gender",
"vocab_list": ["M"]
},
{
"feature_type": "raw_feature",
"expression": "is_member"
},
{
"feature_type": "raw_feature",
"expression": "is_new_userid"
},
{
"feature_type": "id_feature",
"expression": "province",
"hash_bucket_size": 36
},
{
"feature_type": "id_feature",
"expression": "constellation",
"vocab_list": ["巨蟹座", "金牛座", "摩羯座", "狮子座", "双子座", "白羊座", "天蝎座", "双鱼座", "射手座", "水瓶座", "处女座", "天秤座"]
},
{
"feature_type": "id_feature",
"expression": "birthyear",
"boundaries": [1967, 1971, 1974, 1977, 1980, 1982, 1985, 1988, 1992, 2001]
},
{
"feature_type": "id_feature",
"expression": "fans_num",
"boundaries": [2, 7, 16, 29, 54, 104, 210, 455, 1022]
},
{
"feature_type": "id_feature",
"expression": "follow_num",
"boundaries": [1, 4, 9, 17, 30, 53, 99, 214, 544]
},
{
"feature_type": "id_feature",
"expression": "visitor_num",
"boundaries": [0, 58, 157, 349, 795, 1865, 4796, 12664, 35753]
},
{
"feature_type": "id_feature",
"expression": "level",
"num_buckets": 50,
"offset": 1
},
{
"feature_type": "geohash_feature",
"expression": ["latitude", "longitude"],
"geohash_precision": 4,
"hash_bucket_size": 128
},
{
"feature_type": "id_feature",
"expression": "familyid",
"hash_bucket_size": 50
},
{
"feature_type": "id_feature",
"expression": "userid_exposure_cnt_distinct_svid_15",
"boundaries": [63, 159, 252, 348, 439, 508, 580, 679, 878]
},
{
"feature_type": "id_feature",
"expression": "userid_click_cnt_15",
"boundaries": [0, 3, 7, 13, 19, 27, 42, 72, 151]
},
{
"feature_type": "binary_feature",
"expression": "os",
"vocab_list": ["ios"]
},
{
"feature_type": "combo_feature",
"expression": ["os", "os_version"],
"hash_bucket_size": 100
},
{
"feature_type": "id_feature",
"expression": "userid_likes_comment_collect_share_cnt_15",
"boundaries": [0, 1, 2, 3, 6, 11, 27]
},
{
"feature_type": "id_feature",
"expression": "userid_max_duration_15",
"boundaries": [13.3, 44, 80.8, 137, 200.8, 240.4, 276.9, 317.9, 443.9]
},
{
"feature_type": "id_feature",
"expression": "userid_min_duration_15",
"boundaries": [0, 0.1, 5.1, 6.2, 7, 8, 9.7, 12.7]
},
{
"feature_type": "id_feature",
"expression": "userid_avg_duration_15",
"boundaries": [11, 20.15, 25.2857, 29.3875, 32.8862, 36.2385, 39.915, 44.7783, 54.0227]
},
{
"feature_type": "id_feature",
"expression": "author_play_ct",
"boundaries": [0, 50, 356, 2130]
},
{
"feature_type": "id_feature",
"expression": "user_auth_type",
"vocab_list": ["0", "1", "13", "24", "14", "20", "30", "11", "5", "15", "3", "16", "12", "2", "10", "29", "19", "4", "17", "21", "31", "25", "7", "8", "9"]
},
{
"feature_type": "id_feature",
"expression": "author_av_ct",
"boundaries": [0, 1, 2, 5, 14, 34, 90]
},
{
"feature_type": "id_feature",
"expression": "flower_ct",
"boundaries": [0, 72, 347, 1070, 2610, 6463, 16157]
},
{
"feature_type": "id_feature",
"expression": "userid_avg_hot_15",
"boundaries": [0, 0.018, 0.0741, 0.25, 8.53, 46.37, 94.11, 159.86]
},
{
"feature_type": "id_feature",
"expression": "diamond_ct",
"boundaries": [0, 10, 50, 160, 460, 1332, 4910]
},
{
"feature_type": "id_feature",
"expression": "userid_exposure_cnt_distinct_svid_7",
"boundaries": [36, 93, 146, 201, 248, 285, 332, 399, 521]
},
{
"feature_type": "id_feature",
"expression": "family_member_ct",
"boundaries": [0, 82, 159, 234, 322, 438, 642, 1113]
},
{
"feature_type": "id_feature",
"expression": "author_sv_ct",
"boundaries": [0, 2, 8, 34]
},
{
"feature_type": "id_feature",
"expression": "userid_exposure_cnt_15",
"boundaries": [63, 162, 257, 354, 446, 514, 587, 687, 890]
}
]
}
}
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