Taobao / meta_data.json
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{
"dataset": "Ali_Display_Ad_Click / Taobao",
"sample_size": {
"total": 23601301,
"train": 17822296,
"valid": 2871767,
"test": 2907238,
"phase1_total_interactions": 26557961
},
"split_by_date": {
"timezone": "Asia/Shanghai",
"timestamp_unit": "s",
"train_days": 6,
"train_range": "2017-05-06 ~ 2017-05-11",
"valid_days": 1,
"valid_range": "2017-05-12 ~ 2017-05-12",
"test_days": 1,
"test_range": "2017-05-13 ~ 2017-05-13",
"rule": "按 raw_sample 日期排序后按 train/valid/test 比例切分;官方 8 天数据默认约为 6/1/1。"
},
"behavior_log_usage": {
"used": true,
"label_columns": [
"cart",
"fav",
"buy"
],
"stats": {
"raw_rows": 723268134,
"kept_rows": 33592031,
"label_counts": {
"cart": 15943875,
"fav": 9299893,
"buy": 8357307
}
},
"match_key": [
"user_id",
"cate_id",
"brand"
],
"behavior_label_window_seconds": 86400,
"rule": "raw_sample 先通过 adgroup_id 映射到 ad_feature.cate_id/brand;对每条曝光样本,若同一 user_id + cate_id + brand 在曝光 time_stamp 之后 behavior_label_window_seconds 秒内出现对应 btag,则该行为标签置为 1,否则为 0。",
"note": "behavior_log 不含 adgroup_id,因此这里是类目/品牌粒度、曝光后时间窗口内的行为监督,不是广告 ID 粒度的直接标签。"
},
"user_filtering": {
"min_user_interactions": 10,
"valid_users": 470570,
"dropped_users": 671159
},
"oov_filter": {
"min_feat_count": 2,
"applied_to": [
"cms_segid",
"cms_group_id",
"final_gender_code",
"age_level",
"pvalue_level",
"shopping_level",
"occupation",
"new_user_class_level",
"cate_id",
"campaign_id",
"customer_id",
"brand",
"price_bucket"
],
"rule": "特征值出现次数 < min_feat_count 的统一映射为 0 (unknown/padding)",
"freq_source": "基于 raw_sample 中 user/item 出现次数 * 静态特征值统计"
},
"blocked_layout": {
"n_user_parts": 32,
"train_blocks": 32,
"valid_blocks": 8,
"test_blocks": 8,
"train": {
"data_dir": "train/data",
"user_info_dir": "train/user_info",
"item_info_dir": "train/item_info"
},
"valid": {
"data_dir": "valid/data",
"user_info_dir": "valid/user_info",
"item_info_dir": "valid/item_info"
},
"test": {
"data_dir": "test/data",
"user_info_dir": "test/user_info",
"item_info_dir": "test/item_info"
},
"block_pair_rule": "同一 split 下,data/user_info/item_info 使用相同 part-xxxxx 编号配对读取。",
"local_index_rule": {
"user_index": "block-local dense index, starts from 0",
"item_index": "block-local dense index, 0 reserved for padding",
"user_id": "global feature id, consistent across blocks",
"item_id": "global feature id, consistent across blocks"
}
},
"vocab_size": {
"user_index": 470570,
"item_index": 846812,
"user_id": 470571,
"item_id": 846812,
"action": 17,
"timestamp": 0,
"is_weekend": 3,
"hour": 25,
"cms_segid": 99,
"cms_group_id": 15,
"final_gender_code": 4,
"age_level": 9,
"pvalue_level": 5,
"shopping_level": 5,
"occupation": 4,
"new_user_class_level": 6,
"cate_id": 6409,
"campaign_id": 354106,
"customer_id": 220826,
"brand": 88145,
"price_bucket": 11,
"pid": 4
},
"label": [
"is_click",
"cart",
"fav",
"buy"
],
"action_vocab": {
"buy": 1,
"cart": 2,
"cart|buy": 3,
"cart|fav": 4,
"cart|fav|buy": 5,
"exposure": 6,
"fav": 7,
"fav|buy": 8,
"is_click": 9,
"is_click|buy": 10,
"is_click|cart": 11,
"is_click|cart|buy": 12,
"is_click|cart|fav": 13,
"is_click|cart|fav|buy": 14,
"is_click|fav": 15,
"is_click|fav|buy": 16
},
"action_vocab_desc": "编码后的多任务 action 词表,用于 dataloader 基于 full_action_seq 构造 task-specific token masks。",
"user_info_schema": {
"fields": [
"user_index",
"full_item_seq",
"full_action_seq",
"full_timestamp_seq"
],
"full_timestamp_seq_desc": "按时间顺序排列的 raw_sample time_stamp 序列",
"desc": "user_index / full_item_seq 中的 item index 为 block-local index;full_action_seq / full_timestamp_seq 为全局时间顺序序列。"
},
"item_info_schema": {
"fields": [
"item_index",
"item_id",
"cate_id",
"campaign_id",
"customer_id",
"brand",
"price_bucket"
],
"desc": "item_index 为 block-local index;item_id 为全局 item feature id。"
},
"feature_schema": {
"user_static_features": [
"cms_segid",
"cms_group_id",
"final_gender_code",
"age_level",
"pvalue_level",
"shopping_level",
"occupation",
"new_user_class_level"
],
"context_features": [
"pid",
"is_weekend",
"hour"
],
"item_static_features": [
"cate_id",
"campaign_id",
"customer_id",
"brand",
"price_bucket"
],
"label_columns": [
"is_click",
"cart",
"fav",
"buy"
]
},
"max_len": {
"full_item_seq": 3756,
"full_action_seq": 3756,
"full_timestamp_seq": 3756
}
}