import json import time import multiprocessing import traceback import hashlib from datetime import datetime import config from log import Log from config import set_config from video_recall import PoolRecall from video_rank import video_rank, bottom_strategy, video_rank_by_w_h_rate, video_rank_with_old_video, bottom_strategy2 from db_helper import RedisHelper import gevent from utils import FilterVideos import ast log_ = Log() config_ = set_config() def relevant_video_top_recommend(request_id, app_type, mid, uid, head_vid, videos, size): """ 相关推荐强插 运营给定置顶相关性视频 :param request_id: request_id :param app_type: 产品标识 type-int :param mid: mid :param uid: uid :param head_vid: 相关推荐头部视频id type-int :param videos: 当前相关推荐结果 type-list :param size: 返回视频个数 type-int :return: rank_result """ # 获取头部视频对应的相关性视频 key_name = '{}{}'.format(config_.RELEVANT_VIDEOS_WITH_OP_KEY_NAME, head_vid) redis_helper = RedisHelper() relevant_videos = redis_helper.get_data_from_redis(key_name=key_name) if relevant_videos is None: # 该视频没有指定的相关性视频 return videos relevant_videos = json.loads(relevant_videos) # 按照指定顺序排序 relevant_videos_sorted = sorted(relevant_videos, key=lambda x: x['order'], reverse=False) # 过滤 relevant_video_ids = [int(item['recommend_vid']) for item in relevant_videos_sorted] filter_helper = FilterVideos(request_id=request_id, app_type=app_type, video_ids=relevant_video_ids, mid=mid, uid=uid) filtered_ids = filter_helper.filter_videos() if filtered_ids is None: return videos # 获取生效中的视频 now = int(time.time()) relevant_videos_in_effect = [ {'videoId': int(item['recommend_vid']), 'pushFrom': config_.PUSH_FROM['relevant_video_op'], 'abCode': config_.AB_CODE['relevant_video_op']} for item in relevant_videos_sorted if item['start_time'] < now < item['finish_time'] and int(item['recommend_vid']) in filtered_ids ] if len(relevant_videos_in_effect) == 0: return videos # 与现有排序结果 进行合并重排 # 获取现有排序结果中流量池视频 及其位置 relevant_ids = [item['videoId'] for item in relevant_videos_in_effect] flow_pool_videos = [] other_videos = [] for i, item in enumerate(videos): if item.get('pushFrom', None) == config_.PUSH_FROM['flow_recall'] and item.get('videoId') not in relevant_ids: flow_pool_videos.append((i, item)) elif item.get('videoId') not in relevant_ids: other_videos.append(item) else: continue # 重排，保持流量池视频位置不变 rank_result = relevant_videos_in_effect + other_videos for i, item in flow_pool_videos: rank_result.insert(i, item) return rank_result[:size] def video_position_recommend(request_id, mid, uid, app_type, videos): # videos = video_recommend(mid=mid, uid=uid, size=size, app_type=app_type, # algo_type=algo_type, client_info=client_info) redis_helper = RedisHelper() pos1_vids = redis_helper.get_data_from_redis(config.BaseConfig.RECALL_POSITION1_KEY_NAME) pos2_vids = redis_helper.get_data_from_redis(config.BaseConfig.RECALL_POSITION2_KEY_NAME) if pos1_vids is not None: pos1_vids = ast.literal_eval(pos1_vids) if pos2_vids is not None: pos2_vids = ast.literal_eval(pos2_vids) pos1_vids = [] if pos1_vids is None else pos1_vids pos2_vids = [] if pos2_vids is None else pos2_vids pos1_vids = [int(i) for i in pos1_vids] pos2_vids = [int(i) for i in pos2_vids] filter_1 = FilterVideos(request_id=request_id, app_type=app_type, video_ids=pos1_vids, mid=mid, uid=uid) filter_2 = FilterVideos(request_id=request_id, app_type=app_type, video_ids=pos2_vids, mid=mid, uid=uid) t = [gevent.spawn(filter_1.filter_videos), gevent.spawn(filter_2.filter_videos)] gevent.joinall(t) filted_list = [i.get() for i in t] pos1_vids = filted_list[0] pos2_vids = filted_list[1] videos = positon_duplicate(pos1_vids, pos2_vids, videos) if pos1_vids is not None and len(pos1_vids) >0 : videos.insert(0, {'videoId': int(pos1_vids[0]), 'rovScore': 100, 'pushFrom': config_.PUSH_FROM['position_insert'], 'abCode': config_.AB_CODE['position_insert']}) if pos2_vids is not None and len(pos2_vids) >0 : videos.insert(1, {'videoId': int(pos2_vids[0]), 'rovScore': 100, 'pushFrom': config_.PUSH_FROM['position_insert'], 'abCode': config_.AB_CODE['position_insert']}) return videos[:10] def positon_duplicate(pos1_vids, pos2_vids, videos): s = set() if pos1_vids is not None and len(pos1_vids) >0: s.add(int(pos1_vids[0])) if pos2_vids is not None and len(pos2_vids) >0: s.add(int(pos2_vids[0])) l = [] for item in videos: if item['videoId'] in s: continue else: l.append(item) return l def video_recommend(request_id, mid, uid, size, top_K, flow_pool_P, app_type, algo_type, client_info, expire_time=24*3600, ab_code=config_.AB_CODE['initial'], rule_key='', data_key='', no_op_flag=False, old_video_index=-1, video_id=None, params=None): """ 首页线上推荐逻辑 :param request_id: request_id :param mid: mid type-string :param uid: uid type-string :param size: 请求视频数量 type-int :param top_K: 保证topK为召回池视频 type-int :param flow_pool_P: size-top_K视频为流量池视频的概率 type-float :param app_type: 产品标识 type-int :param algo_type: 算法类型 type-string :param client_info: 用户位置信息 {"country": "国家", "province": "省份", "city": "城市"} :param expire_time: 末位视频记录redis过期时间 :param ab_code: AB实验code :param video_id: 相关推荐头部视频id :param params: :return: """ # ####### 多进程召回 start_recall = time.time() # log_.info('====== recall') ''' cores = multiprocessing.cpu_count() pool = multiprocessing.Pool(processes=cores) pool_recall = PoolRecall(app_type=app_type, mid=mid, uid=uid, ab_code=ab_code) _, last_rov_recall_key, _ = pool_recall.get_video_last_idx() pool_list = [ # rov召回池 pool.apply_async(pool_recall.rov_pool_recall, (size,)), # 流量池 pool.apply_async(pool_recall.flow_pool_recall, (size,)) ] recall_result_list = [p.get() for p in pool_list] pool.close() pool.join() ''' recall_result_list = [] pool_recall = PoolRecall(request_id=request_id, app_type=app_type, mid=mid, uid=uid, ab_code=ab_code, client_info=client_info, rule_key=rule_key, data_key=data_key, no_op_flag=no_op_flag, params=params) _, last_rov_recall_key, _ = pool_recall.get_video_last_idx() # # 小时级实验 # if ab_code in [code for _, code in config_.AB_CODE['rank_by_h'].items()]: # t = [gevent.spawn(pool_recall.rule_recall_by_h, size, expire_time), # gevent.spawn(pool_recall.flow_pool_recall, size, config_.QUICK_FLOW_POOL_ID), # gevent.spawn(pool_recall.flow_pool_recall, size)] # # 小时级实验 # elif ab_code in [code for _, code in config_.AB_CODE['rank_by_24h'].items()]: # t = [gevent.spawn(pool_recall.rov_pool_recall_by_h, size, expire_time), # gevent.spawn(pool_recall.flow_pool_recall, size, config_.QUICK_FLOW_POOL_ID), # gevent.spawn(pool_recall.flow_pool_recall, size)] # 地域分组实验 if ab_code in [code for _, code in config_.AB_CODE['region_rank_by_h'].items()]: if app_type in [config_.APP_TYPE['LAO_HAO_KAN_VIDEO'], config_.APP_TYPE['ZUI_JING_QI']]: t = [gevent.spawn(pool_recall.rov_pool_recall_with_region, size, expire_time)] else: t = [gevent.spawn(pool_recall.rov_pool_recall_with_region, size, expire_time), gevent.spawn(pool_recall.flow_pool_recall, size, config_.QUICK_FLOW_POOL_ID), gevent.spawn(pool_recall.flow_pool_recall, size)] # 最惊奇相关推荐实验 # elif ab_code == config_.AB_CODE['top_video_relevant_appType_19']: # t = [gevent.spawn(pool_recall.relevant_recall_19, video_id, size, expire_time), # gevent.spawn(pool_recall.flow_pool_recall_18_19, size)] # 最惊奇完整影视实验 # elif ab_code == config_.AB_CODE['whole_movies']: # t = [gevent.spawn(pool_recall.rov_pool_recall_19, size, expire_time)] # 最惊奇/老好看实验 # elif app_type in [config_.APP_TYPE['LAO_HAO_KAN_VIDEO'], config_.APP_TYPE['ZUI_JING_QI']]: # t = [gevent.spawn(pool_recall.rov_pool_recall, size, expire_time), # gevent.spawn(pool_recall.flow_pool_recall_18_19, size)] # # 天级实验 # elif ab_code in [code for _, code in config_.AB_CODE['rank_by_day'].items()]: # t = [gevent.spawn(pool_recall.rov_pool_recall_by_day, size, expire_time), # gevent.spawn(pool_recall.flow_pool_recall, size, config_.QUICK_FLOW_POOL_ID), # gevent.spawn(pool_recall.flow_pool_recall, size)] # 老视频实验 # elif ab_code in [config_.AB_CODE['old_video']]: # t = [gevent.spawn(pool_recall.rov_pool_recall, size, expire_time), # gevent.spawn(pool_recall.flow_pool_recall, size), # gevent.spawn(pool_recall.old_videos_recall, size)] else: if app_type in [config_.APP_TYPE['LAO_HAO_KAN_VIDEO'], config_.APP_TYPE['ZUI_JING_QI']]: t = [gevent.spawn(pool_recall.rov_pool_recall, size, expire_time)] else: t = [gevent.spawn(pool_recall.rov_pool_recall, size, expire_time), gevent.spawn(pool_recall.flow_pool_recall, size, config_.QUICK_FLOW_POOL_ID), gevent.spawn(pool_recall.flow_pool_recall, size)] gevent.joinall(t) recall_result_list = [i.get() for i in t] # end_recall = time.time() log_.info({ 'logTimestamp': int(time.time() * 1000), 'request_id': request_id, 'mid': mid, 'uid': uid, 'operation': 'recall', 'recall_result': recall_result_list, 'executeTime': (time.time() - start_recall) * 1000 }) # ####### 排序 start_rank = time.time() # log_.info('====== rank') if app_type in [config_.APP_TYPE['LAO_HAO_KAN_VIDEO'], config_.APP_TYPE['ZUI_JING_QI']]: if ab_code in [ config_.AB_CODE['rov_rank_appType_18_19'], config_.AB_CODE['rov_rank_appType_19'], config_.AB_CODE['top_video_relevant_appType_19'] ]: data = { 'rov_pool_recall': recall_result_list[0], 'flow_pool_recall': recall_result_list[1] } else: data = { 'rov_pool_recall': recall_result_list[0], 'flow_pool_recall': [] } else: if recall_result_list[1]: redis_helper = RedisHelper() quick_flow_pool_P = redis_helper.get_data_from_redis( key_name=f"{config_.QUICK_FLOWPOOL_DISTRIBUTE_RATE_KEY_NAME_PREFIX}{config_.QUICK_FLOW_POOL_ID}" ) if quick_flow_pool_P: flow_pool_P = quick_flow_pool_P data = { 'rov_pool_recall': recall_result_list[0], 'flow_pool_recall': recall_result_list[1] } else: data = { 'rov_pool_recall': recall_result_list[0], 'flow_pool_recall': recall_result_list[2] } rank_result = video_rank(data=data, size=size, top_K=top_K, flow_pool_P=float(flow_pool_P)) # 老视频实验 # if ab_code in [config_.AB_CODE['old_video']]: # rank_result = video_rank_with_old_video(rank_result=rank_result, old_video_recall=recall_result_list[2], # size=size, top_K=top_K, old_video_index=old_video_index) # end_rank = time.time() log_.info({ 'logTimestamp': int(time.time() * 1000), 'request_id': request_id, 'mid': mid, 'uid': uid, 'operation': 'rank', 'rank_result': rank_result, 'executeTime': (time.time() - start_rank) * 1000 }) if not rank_result: # 兜底策略 # log_.info('====== bottom strategy') start_bottom = time.time() if ab_code == config_.AB_CODE['region_rank_by_h'].get('abtest_130'): rank_result = bottom_strategy2( size=size, app_type=app_type, mid=mid, uid=uid, ab_code=ab_code, client_info=client_info, params=params ) else: rank_result = bottom_strategy( request_id=request_id, size=size, app_type=app_type, ab_code=ab_code, params=params ) log_.info({ 'logTimestamp': int(time.time() * 1000), 'request_id': request_id, 'mid': mid, 'uid': uid, 'operation': 'bottom', 'bottom_result': rank_result, 'executeTime': (time.time() - start_bottom) * 1000 }) return rank_result, last_rov_recall_key def ab_test_op(rank_result, ab_code_list, app_type, mid, uid, **kwargs): """ 对排序后的结果 按照AB实验进行对应的分组操作 :param rank_result: 排序后的结果 :param ab_code_list: 此次请求参与的 ab实验组 :param app_type: 产品标识 :param mid: mid :param uid: uid :param kwargs: 其他参数 :return: """ # ####### 视频宽高比AB实验 # 对内容精选进行 视频宽高比分发实验 # if config_.AB_CODE['w_h_rate'] in ab_code_list and app_type in config_.AB_TEST.get('w_h_rate', []): # rank_result = video_rank_by_w_h_rate(videos=rank_result) # log_.info('app_type: {}, mid: {}, uid: {}, rank_by_w_h_rate_result: {}'.format( # app_type, mid, uid, rank_result)) # 按position位置排序 if config_.AB_CODE['position_insert'] in ab_code_list and app_type in config_.AB_TEST.get('position_insert', []): rank_result = video_position_recommend(mid, uid, app_type, rank_result) print('===========================') print(rank_result) log_.info('app_type: {}, mid: {}, uid: {}, rank_by_position_insert_result: {}'.format( app_type, mid, uid, rank_result)) # 相关推荐强插 # if config_.AB_CODE['relevant_video_op'] in ab_code_list \ # and app_type in config_.AB_TEST.get('relevant_video_op', []): # head_vid = kwargs['head_vid'] # size = kwargs['size'] # rank_result = relevant_video_top_recommend( # app_type=app_type, mid=mid, uid=uid, head_vid=head_vid, videos=rank_result, size=size # ) # log_.info('app_type: {}, mid: {}, uid: {}, head_vid: {}, rank_by_relevant_video_op_result: {}'.format( # app_type, mid, uid, head_vid, rank_result)) return rank_result def update_redis_data(result, app_type, mid, last_rov_recall_key, top_K, expire_time=24*3600): """ 根据最终的排序结果更新相关redis数据 :param result: 排序结果 :param app_type: 产品标识 :param mid: mid :param last_rov_recall_key: 用户上一次在rov召回池对应的位置 redis key :param top_K: 保证topK为召回池视频 type-int :param expire_time: 末位视频记录redis过期时间 :return: None """ # ####### redis数据刷新 try: redis_helper = RedisHelper() # log_.info('====== update redis') if mid and mid != 'null': # mid为空时，不做预曝光和定位数据更新 # 预曝光数据同步刷新到Redis, 过期时间为0.5h preview_key_name = f"{config_.PREVIEW_KEY_PREFIX}{app_type}:{mid}" preview_video_ids = [int(item['videoId']) for item in result] if preview_video_ids: # log_.error('key_name = {} \n values = {}'.format(preview_key_name, tuple(preview_video_ids))) redis_helper.add_data_with_set(key_name=preview_key_name, values=tuple(preview_video_ids), expire_time=30 * 60) # log_.info('preview redis update success!') # 将此次获取的ROV召回池top_K末位视频id同步刷新到Redis中，方便下次快速定位到召回位置，过期时间为1天 rov_recall_video = [item['videoId'] for item in result[:top_K] if item['pushFrom'] == config_.PUSH_FROM['rov_recall']] if len(rov_recall_video) > 0: if app_type == config_.APP_TYPE['APP']: key_name = config_.UPDATE_ROV_KEY_NAME_APP else: key_name = config_.UPDATE_ROV_KEY_NAME if not redis_helper.get_score_with_value(key_name=key_name, value=rov_recall_video[-1]): redis_helper.set_data_to_redis(key_name=last_rov_recall_key, value=rov_recall_video[-1], expire_time=expire_time) # log_.info('last video redis update success!') # 将此次获取的 地域分组小时级数据列表 中的视频id同步刷新到redis中，方便下次快速定位到召回位置 rov_recall_h_video = [item['videoId'] for item in result[:top_K] if item['pushFrom'] == config_.PUSH_FROM['rov_recall_region_h']] if len(rov_recall_h_video) > 0: last_video_key = f'{config_.LAST_VIDEO_FROM_REGION_H_PREFIX}{app_type}:{mid}' redis_helper.set_data_to_redis(key_name=last_video_key, value=rov_recall_h_video[-1], expire_time=expire_time) # 将此次获取的 地域分组相对24h数据列表 中的视频id同步刷新到redis中，方便下次快速定位到召回位置 rov_recall_24h_dup1_video = [item['videoId'] for item in result[:top_K] if item['pushFrom'] == config_.PUSH_FROM['rov_recall_region_24h']] if len(rov_recall_24h_dup1_video) > 0: last_video_key = f'{config_.LAST_VIDEO_FROM_REGION_DUP1_24H_PREFIX}{app_type}:{mid}' redis_helper.set_data_to_redis(key_name=last_video_key, value=rov_recall_24h_dup1_video[-1], expire_time=expire_time) # 将此次获取的 相对24h筛选数据列表 中的视频id同步刷新到redis中，方便下次快速定位到召回位置 rov_recall_24h_dup2_video = [item['videoId'] for item in result[:top_K] if item['pushFrom'] == config_.PUSH_FROM['rov_recall_24h']] if len(rov_recall_24h_dup2_video) > 0: last_video_key = f'{config_.LAST_VIDEO_FROM_REGION_DUP2_24H_PREFIX}{app_type}:{mid}' redis_helper.set_data_to_redis(key_name=last_video_key, value=rov_recall_24h_dup2_video[-1], expire_time=expire_time) # 将此次获取的 相对24h筛选后剩余数据列表 中的视频id同步刷新到redis中，方便下次快速定位到召回位置 rov_recall_24h_dup3_video = [item['videoId'] for item in result[:top_K] if item['pushFrom'] == config_.PUSH_FROM['rov_recall_24h_dup']] if len(rov_recall_24h_dup3_video) > 0: last_video_key = f'{config_.LAST_VIDEO_FROM_REGION_DUP3_24H_PREFIX}{app_type}:{mid}' redis_helper.set_data_to_redis(key_name=last_video_key, value=rov_recall_24h_dup3_video[-1], expire_time=expire_time) # 将此次获取的 相对48h筛选数据列表 中的视频id同步刷新到redis中，方便下次快速定位到召回位置 rov_recall_48h_dup2_video = [item['videoId'] for item in result[:top_K] if item['pushFrom'] == config_.PUSH_FROM['rov_recall_48h']] if len(rov_recall_48h_dup2_video) > 0: last_video_key = f'{config_.LAST_VIDEO_FROM_REGION_DUP2_48H_PREFIX}{app_type}:{mid}' redis_helper.set_data_to_redis(key_name=last_video_key, value=rov_recall_48h_dup2_video[-1], expire_time=expire_time) # 将此次获取的 相对48h筛选后剩余数据列表 中的视频id同步刷新到redis中，方便下次快速定位到召回位置 rov_recall_48h_dup3_video = [item['videoId'] for item in result[:top_K] if item['pushFrom'] == config_.PUSH_FROM['rov_recall_48h_dup']] if len(rov_recall_48h_dup3_video) > 0: last_video_key = f'{config_.LAST_VIDEO_FROM_REGION_DUP3_48H_PREFIX}{app_type}:{mid}' redis_helper.set_data_to_redis(key_name=last_video_key, value=rov_recall_48h_dup3_video[-1], expire_time=expire_time) # 将此次分发的流量池视频，对 本地分发数-1 进行记录 if app_type not in [config_.APP_TYPE['LAO_HAO_KAN_VIDEO'], config_.APP_TYPE['ZUI_JING_QI']]: flow_recall_video = [item for item in result if item['pushFrom'] == config_.PUSH_FROM['flow_recall']] if flow_recall_video: update_local_distribute_count(flow_recall_video) # log_.info('update local distribute count success!') # 限流视频分发数记录 if app_type == config_.APP_TYPE['APP']: # APP 不计入 return limit_video_id_list = redis_helper.get_data_from_set( key_name=f"{config_.KEY_NAME_PREFIX_LIMIT_VIDEO_SET}{datetime.today().strftime('%Y%m%d')}" ) if limit_video_id_list is not None: limit_video_id_list = [int(item) for item in limit_video_id_list] for item in result: video_id = item['videoId'] if video_id in limit_video_id_list: key_name = f"{config_.KEY_NAME_PREFIX_LIMIT_VIDEO_DISTRIBUTE_COUNT}{video_id}" redis_helper.setnx_key(key_name=key_name, value=0, expire_time=24*2600) redis_helper.incr_key(key_name=key_name, amount=1, expire_time=24*3600) except Exception as e: log_.error("update redis data fail!") log_.error(traceback.format_exc()) def update_local_distribute_count(videos): """ 更新本地分发数 :param videos: 视频列表 type-list [{'videoId':'', 'flowPool':'', 'distributeCount': '', 'rovScore': '', 'pushFrom': 'flow_pool', 'abCode': self.ab_code}, ....] :return: """ try: redis_helper = RedisHelper() for item in videos: key_name = f"{config_.LOCAL_DISTRIBUTE_COUNT_PREFIX}{item['videoId']}:{item['flowPool']}" # 本地记录的分发数 - 1 redis_helper.decr_key(key_name=key_name, amount=1, expire_time=15 * 60) # if redis_helper.key_exists(key_name=key_name): # # 该视频本地有记录，本地记录的分发数 - 1 # redis_helper.decr_key(key_name=key_name, amount=1, expire_time=5 * 60) # else: # # 该视频本地无记录，接口获取的分发数 - 1 # redis_helper.incr_key(key_name=key_name, amount=int(item['distributeCount']) - 1, expire_time=5 * 60) except Exception as e: log_.error('update_local_distribute_count error...') log_.error(traceback.format_exc()) def get_recommend_params(recommend_type, ab_exp_info, ab_info_data, page_type=0): """ 根据实验分组给定对应的推荐参数 :param recommend_type: 首页推荐和相关推荐区分参数（0-首页推荐，1-相关推荐） :param ab_exp_info: AB实验组参数 :param ab_info_data: app实验组参数 :param page_type: 页面区分参数，默认：0（首页） :return: """ top_K = config_.K flow_pool_P = config_.P # 不获取人工干预数据标记 no_op_flag = False old_video_index = -1 # if not ab_exp_info: # ab_code = config_.AB_CODE['initial'] # expire_time = 24 * 3600 # rule_key = config_.RULE_KEY_REGION['initial'].get('rule_key') # data_key = config_.RULE_KEY_REGION['initial'].get('data_key') # # old_video_index = -1 # else: # 默认使用 095 实验的配置 ab_code = config_.AB_EXP_CODE['095'].get('ab_code') expire_time = 3600 rule_key = config_.AB_EXP_CODE['095'].get('rule_key') data_key = config_.AB_EXP_CODE['095'].get('data_key') no_op_flag = True # 获取实验配置 if ab_exp_info: ab_exp_code_list = [] config_value_dict = {} for _, item in ab_exp_info.items(): if not item: continue for ab_item in item: ab_exp_code = ab_item.get('abExpCode', None) if not ab_exp_code: continue ab_exp_code_list.append(str(ab_exp_code)) config_value_dict[str(ab_exp_code)] = ab_item.get('configValue', None) for code, param in config_.AB_EXP_CODE.items(): if code in ab_exp_code_list: ab_code = param.get('ab_code') expire_time = 3600 rule_key = param.get('rule_key') data_key = param.get('data_key') no_op_flag = True break """ # 推荐条数 10->4 实验 # if config_.AB_EXP_CODE['rec_size_home'] in ab_exp_code_list: # config_value = config_value_dict.get(config_.AB_EXP_CODE['rec_size_home'], None) # if config_value: # config_value = eval(str(config_value)) # else: # config_value = {} # log_.info(f'config_value: {config_value}, type: {type(config_value)}') # size = int(config_value.get('size', 4)) # top_K = int(config_value.get('K', 3)) # flow_pool_P = float(config_value.get('P', 0.3)) # else: # size = size # top_K = config_.K # flow_pool_P = config_.P # 算法实验相对对照组 # if config_.AB_EXP_CODE['ab_initial'] in ab_exp_code_list: # ab_code = config_.AB_CODE['ab_initial'] # expire_time = 24 * 3600 # rule_key = config_.RULE_KEY['initial'] # no_op_flag = True # 小时级更新-规则1 实验 # elif config_.AB_EXP_CODE['rule_rank1'] in ab_exp_code_list: # ab_code = config_.AB_CODE['rank_by_h'].get('rule_rank1') # expire_time = 3600 # rule_key = config_.RULE_KEY['rule_rank1'] # no_op_flag = True # elif config_.AB_EXP_CODE['rule_rank2'] in ab_exp_code_list: # ab_code = config_.AB_CODE['rank_by_h'].get('rule_rank2') # expire_time = 3600 # rule_key = config_.RULE_KEY['rule_rank2'] # elif config_.AB_EXP_CODE['rule_rank3'] in ab_exp_code_list: # ab_code = config_.AB_CODE['rank_by_h'].get('rule_rank3') # expire_time = 3600 # rule_key = config_.RULE_KEY['rule_rank3'] # no_op_flag = True # elif config_.AB_EXP_CODE['rule_rank4'] in ab_exp_code_list: # ab_code = config_.AB_CODE['rank_by_h'].get('rule_rank4') # expire_time = 3600 # rule_key = config_.RULE_KEY['rule_rank4'] # elif config_.AB_EXP_CODE['rule_rank5'] in ab_exp_code_list: # ab_code = config_.AB_CODE['rank_by_h'].get('rule_rank5') # expire_time = 3600 # rule_key = config_.RULE_KEY['rule_rank5'] # elif config_.AB_EXP_CODE['day_rule_rank1'] in ab_exp_code_list: # ab_code = config_.AB_CODE['rank_by_day'].get('day_rule_rank1') # expire_time = 24 * 3600 # rule_key = config_.RULE_KEY_DAY['day_rule_rank1'] # no_op_flag = True # if config_.AB_EXP_CODE['rule_rank6'] in ab_exp_code_list: # ab_code = config_.AB_CODE['rank_by_h'].get('rule_rank6') # expire_time = 3600 # rule_key = config_.RULE_KEY['rule_rank6'] # no_op_flag = True # elif config_.AB_EXP_CODE['day_rule_rank2'] in ab_exp_code_list: # ab_code = config_.AB_CODE['rank_by_day'].get('day_rule_rank2') # expire_time = 24 * 3600 # rule_key = config_.RULE_KEY_DAY['day_rule_rank2'] # no_op_flag = True # elif config_.AB_EXP_CODE['region_rule_rank1'] in ab_exp_code_list: # ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank1') # expire_time = 3600 # rule_key = config_.RULE_KEY_REGION['region_rule_rank1'] # no_op_flag = True # elif config_.AB_EXP_CODE['24h_rule_rank1'] in ab_exp_code_list: # ab_code = config_.AB_CODE['rank_by_24h'].get('24h_rule_rank1') # expire_time = 3600 # rule_key = config_.RULE_KEY_24H['24h_rule_rank1'] # no_op_flag = True # elif config_.AB_EXP_CODE['24h_rule_rank2'] in ab_exp_code_list: # ab_code = config_.AB_CODE['rank_by_24h'].get('24h_rule_rank2') # expire_time = 3600 # rule_key = config_.RULE_KEY_24H['24h_rule_rank2'] # no_op_flag = True # elif config_.AB_EXP_CODE['region_rule_rank2'] in ab_exp_code_list: # ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank2') # expire_time = 3600 # rule_key = config_.RULE_KEY_REGION['region_rule_rank2'] # no_op_flag = True # if config_.AB_EXP_CODE['region_rule_rank3'] in ab_exp_code_list or\ # config_.AB_EXP_CODE['region_rule_rank3_appType_19'] in ab_exp_code_list or\ # config_.AB_EXP_CODE['region_rule_rank3_appType_4'] in ab_exp_code_list or\ # config_.AB_EXP_CODE['region_rule_rank3_appType_6'] in ab_exp_code_list or\ # config_.AB_EXP_CODE['region_rule_rank3_appType_18'] in ab_exp_code_list: # ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank3') # expire_time = 3600 # rule_key = config_.RULE_KEY_REGION['region_rule_rank3'].get('rule_key') # data_key = config_.RULE_KEY_REGION['region_rule_rank3'].get('data_key') # no_op_flag = True # if config_.AB_EXP_CODE['region_rule_rank4'] in ab_exp_code_list or\ if config_.AB_EXP_CODE['region_rule_rank4_appType_19'] in ab_exp_code_list or \ config_.AB_EXP_CODE['region_rule_rank4_appType_4'] in ab_exp_code_list or\ config_.AB_EXP_CODE['region_rule_rank4_appType_6'] in ab_exp_code_list or\ config_.AB_EXP_CODE['region_rule_rank4_appType_18'] in ab_exp_code_list: ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank4') expire_time = 3600 rule_key = config_.RULE_KEY_REGION['region_rule_rank4'].get('rule_key') data_key = config_.RULE_KEY_REGION['region_rule_rank4'].get('data_key') no_op_flag = True # elif config_.AB_EXP_CODE['region_rule_rank4_appType_5_data1'] in ab_exp_code_list: # ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank4') # expire_time = 3600 # rule_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_5_data1'].get('rule_key') # data_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_5_data1'].get('data_key') # no_op_flag = True # elif config_.AB_EXP_CODE['region_rule_rank3_appType_5_data2'] in ab_exp_code_list: # ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank3_appType_5_data2') # expire_time = 3600 # rule_key = config_.RULE_KEY_REGION['region_rule_rank3_appType_5_data2'].get('rule_key') # data_key = config_.RULE_KEY_REGION['region_rule_rank3_appType_5_data2'].get('data_key') # no_op_flag = True elif config_.AB_EXP_CODE['region_rule_rank4_appType_5_data3'] in ab_exp_code_list: ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank4_appType_5_data3') expire_time = 3600 rule_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_5_data3'].get('rule_key') data_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_5_data3'].get('data_key') no_op_flag = True elif config_.AB_EXP_CODE['region_rule_rank4_appType_5_data4'] in ab_exp_code_list: ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank4_appType_5_data4') expire_time = 3600 rule_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_5_data4'].get('rule_key') data_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_5_data4'].get('data_key') no_op_flag = True elif config_.AB_EXP_CODE['region_rule_rank4_appType_0_data2'] in ab_exp_code_list: ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank4_appType_0_data2') expire_time = 3600 rule_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_0_data2'].get('rule_key') data_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_0_data2'].get('data_key') no_op_flag = True # elif config_.AB_EXP_CODE['region_rule_rank4_appType_19_data2'] in ab_exp_code_list: # ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank4_appType_19_data2') # expire_time = 3600 # rule_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_19_data2'].get('rule_key') # data_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_19_data2'].get('data_key') # no_op_flag = True # elif config_.AB_EXP_CODE['region_rule_rank4_appType_19_data3'] in ab_exp_code_list: # ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank4_appType_19_data3') # expire_time = 3600 # rule_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_19_data3'].get('rule_key') # data_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_19_data3'].get('data_key') # no_op_flag = True elif config_.AB_EXP_CODE['region_rule_rank5_appType_0_data1'] in ab_exp_code_list: ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank5_appType_0_data1') expire_time = 3600 rule_key = config_.RULE_KEY_REGION['region_rule_rank5_appType_0_data1'].get('rule_key') data_key = config_.RULE_KEY_REGION['region_rule_rank5_appType_0_data1'].get('data_key') no_op_flag = True elif config_.AB_EXP_CODE['region_rule_rank4_appType_4_data2'] in ab_exp_code_list: ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank4_appType_4_data2') expire_time = 3600 rule_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_4_data2'].get('rule_key') data_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_4_data2'].get('data_key') no_op_flag = True elif config_.AB_EXP_CODE['region_rule_rank4_appType_4_data3'] in ab_exp_code_list: ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank4_appType_4_data3') expire_time = 3600 rule_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_4_data3'].get('rule_key') data_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_4_data3'].get('data_key') no_op_flag = True elif config_.AB_EXP_CODE['region_rule_rank4_appType_6_data2'] in ab_exp_code_list: ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank4_appType_6_data2') expire_time = 3600 rule_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_6_data2'].get('rule_key') data_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_6_data2'].get('data_key') no_op_flag = True elif config_.AB_EXP_CODE['region_rule_rank4_appType_6_data3'] in ab_exp_code_list: ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank4_appType_6_data3') expire_time = 3600 rule_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_6_data3'].get('rule_key') data_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_6_data3'].get('data_key') no_op_flag = True # elif config_.AB_EXP_CODE['region_rule_rank4_appType_18_data2'] in ab_exp_code_list: # ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank4_appType_18_data2') # expire_time = 3600 # rule_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_18_data2'].get('rule_key') # data_key = config_.RULE_KEY_REGION['region_rule_rank4_appType_18_data2'].get('data_key') # no_op_flag = True # elif config_.AB_EXP_CODE['region_rule_rank6_appType_0_data1'] in ab_exp_code_list: # ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank6_appType_0_data1') # expire_time = 3600 # rule_key = config_.RULE_KEY_REGION['region_rule_rank6_appType_0_data1'].get('rule_key') # data_key = config_.RULE_KEY_REGION['region_rule_rank6_appType_0_data1'].get('data_key') # no_op_flag = True else: ab_code = config_.AB_CODE['initial'] expire_time = 24 * 3600 rule_key = config_.RULE_KEY_REGION['initial'].get('rule_key') data_key = config_.RULE_KEY_REGION['initial'].get('data_key') # # 老好看视频 / 票圈最惊奇 首页/相关推荐逻辑更新实验 # if config_.AB_EXP_CODE['rov_rank_appType_18_19'] in ab_exp_code_list: # ab_code = config_.AB_CODE['rov_rank_appType_18_19'] # expire_time = 3600 # flow_pool_P = config_.P_18_19 # no_op_flag = True # # elif config_.AB_EXP_CODE['rov_rank_appType_19'] in ab_exp_code_list: # ab_code = config_.AB_CODE['rov_rank_appType_19'] # expire_time = 3600 # top_K = 0 # flow_pool_P = config_.P_18_19 # no_op_flag = True # # elif config_.AB_EXP_CODE['top_video_relevant_appType_19'] in ab_exp_code_list and page_type == 2: # ab_code = config_.AB_CODE['top_video_relevant_appType_19'] # expire_time = 3600 # top_K = 1 # flow_pool_P = config_.P_18_19 # no_op_flag = True # # # 票圈最惊奇完整影视资源实验 # elif config_.AB_EXP_CODE['whole_movies'] in ab_exp_code_list: # ab_code = config_.AB_CODE['whole_movies'] # expire_time = 24 * 3600 # no_op_flag = True # 老视频实验 # if config_.AB_EXP_CODE['old_video'] in ab_exp_code_list: # ab_code = config_.AB_CODE['old_video'] # no_op_flag = True # old_video_index = 2 # else: # old_video_index = -1 """ # APP实验组 if ab_info_data: ab_info_app = {} for page_code, item in json.loads(ab_info_data).items(): if not item: continue ab_info_code = item.get('eventId', None) if ab_info_code: ab_info_app[page_code] = ab_info_code # print(f"======{ab_info_app}") # 首页推荐 if recommend_type == 0: app_ab_code = ab_info_app.get('10003', None) for code, param in config_.APP_AB_CODE['10003'].items(): if code == app_ab_code: ab_code = param.get('ab_code') expire_time = 3600 rule_key = param.get('rule_key') data_key = param.get('data_key') no_op_flag = True break # # 相关推荐 # elif recommend_type == 1: # if config_.APP_AB_CODE['10037'] == ab_info_app.get('10037', None): # ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank4') # expire_time = 3600 # rule_key = 'rule3' # data_key = 'data1' # no_op_flag = True return top_K, flow_pool_P, ab_code, rule_key, data_key, expire_time, no_op_flag, old_video_index def video_homepage_recommend(request_id, mid, uid, size, app_type, algo_type, client_info, ab_exp_info, params, ab_info_data, version_audit_status): """ 首页线上推荐逻辑 :param request_id: request_id :param mid: mid type-string :param uid: uid type-string :param size: 请求视频数量 type-int :param app_type: 产品标识 type-int :param algo_type: 算法类型 type-string :param client_info: 用户位置信息 {"country": "国家", "province": "省份", "city": "城市"} :param ab_exp_info: ab实验分组参数 [{"expItemId":1, "configValue":{"size":4, "K":3, ...}}, ...] :param params: :param ab_info_data: app实验分组参数 :param version_audit_status: 小程序版本审核参数：1-审核中，2-审核通过 :return: """ # 对 vlog 切换10%的流量做实验 # 对mid进行哈希 # hash_mid = hashlib.md5(mid.encode('utf-8')).hexdigest() # if app_type in config_.AB_TEST['rank_by_h'] and hash_mid[-1:] in ['8', '0', 'a', 'b']: # # 简单召回 - 排序 - 兜底 # rank_result, last_rov_recall_key = video_recommend(mid=mid, uid=uid, size=size, app_type=app_type, # algo_type=algo_type, client_info=client_info, # expire_time=3600, # ab_code=config_.AB_CODE['rank_by_h']) # # ab-test # result = ab_test_op(rank_result=rank_result, # ab_code_list=[config_.AB_CODE['position_insert']], # app_type=app_type, mid=mid, uid=uid) # # redis数据刷新 # update_redis_data(result=result, app_type=app_type, mid=mid, last_rov_recall_key=last_rov_recall_key, # expire_time=3600) # if app_type == config_.APP_TYPE['APP']: # # 票圈视频APP # top_K = config_.K # flow_pool_P = config_.P # # 简单召回 - 排序 - 兜底 # rank_result, last_rov_recall_key = video_recommend(request_id=request_id, # mid=mid, uid=uid, app_type=app_type, # size=size, top_K=top_K, flow_pool_P=flow_pool_P, # algo_type=algo_type, client_info=client_info, # expire_time=12 * 3600, params=params) # # ab-test # # result = ab_test_op(rank_result=rank_result, # # ab_code_list=[config_.AB_CODE['position_insert']], # # app_type=app_type, mid=mid, uid=uid) # # redis数据刷新 # update_redis_data(result=rank_result, app_type=app_type, mid=mid, last_rov_recall_key=last_rov_recall_key, # top_K=top_K, expire_time=12 * 3600) # # else: param_st = time.time() # 特殊mid 和 小程序审核版本推荐处理 if mid in get_special_mid_list() or version_audit_status == 1: rank_result = special_mid_recommend(request_id=request_id, mid=mid, uid=uid, app_type=app_type, size=size) return rank_result # 普通mid推荐处理 top_K, flow_pool_P, ab_code, rule_key, data_key, expire_time, no_op_flag, old_video_index = \ get_recommend_params(recommend_type=0, ab_exp_info=ab_exp_info, ab_info_data=ab_info_data) log_.info({ 'logTimestamp': int(time.time() * 1000), 'request_id': request_id, 'app_type': app_type, 'mid': mid, 'uid': uid, 'operation': 'get_recommend_params', 'executeTime': (time.time() - param_st) * 1000 }) # 简单召回 - 排序 - 兜底 get_result_st = time.time() rank_result, last_rov_recall_key = video_recommend(request_id=request_id, mid=mid, uid=uid, app_type=app_type, size=size, top_K=top_K, flow_pool_P=flow_pool_P, algo_type=algo_type, client_info=client_info, ab_code=ab_code, expire_time=expire_time, rule_key=rule_key, data_key=data_key, no_op_flag=no_op_flag, old_video_index=old_video_index, params=params) log_.info({ 'logTimestamp': int(time.time() * 1000), 'request_id': request_id, 'app_type': app_type, 'mid': mid, 'uid': uid, 'operation': 'get_recommend_result', 'executeTime': (time.time() - get_result_st) * 1000 }) # ab-test # result = ab_test_op(rank_result=rank_result, # ab_code_list=[config_.AB_CODE['position_insert']], # app_type=app_type, mid=mid, uid=uid) # redis数据刷新 update_redis_st = time.time() update_redis_data(result=rank_result, app_type=app_type, mid=mid, last_rov_recall_key=last_rov_recall_key, top_K=top_K) log_.info({ 'logTimestamp': int(time.time() * 1000), 'request_id': request_id, 'app_type': app_type, 'mid': mid, 'uid': uid, 'operation': 'update_redis_data', 'executeTime': (time.time() - update_redis_st) * 1000 }) return rank_result def video_relevant_recommend(request_id, video_id, mid, uid, size, app_type, ab_exp_info, client_info, page_type, params, ab_info_data, version_audit_status): """ 相关推荐逻辑 :param request_id: request_id :param video_id: 相关推荐的头部视频id :param mid: mid type-string :param uid: uid type-string :param size: 请求视频数量 type-int :param app_type: 产品标识 type-int :param ab_exp_info: ab实验分组参数 [{"expItemId":1, "configValue":{"size":4, "K":3, ...}}, ...] :param client_info: 地域参数 :param page_type: 页面区分参数 1:详情页；2:分享页 :param params: :param ab_info_data: app实验分组参数 :param version_audit_status: 小程序版本审核参数：1-审核中，2-审核通过 :return: videos type-list """ param_st = time.time() # 特殊mid 和 小程序审核版本推荐处理 if mid in get_special_mid_list() or version_audit_status == 1: rank_result = special_mid_recommend(request_id=request_id, mid=mid, uid=uid, app_type=app_type, size=size) return rank_result # 普通mid推荐处理 top_K, flow_pool_P, ab_code, rule_key, data_key, expire_time, no_op_flag, old_video_index = \ get_recommend_params(recommend_type=1, ab_exp_info=ab_exp_info, ab_info_data=ab_info_data, page_type=page_type) log_.info({ 'logTimestamp': int(time.time() * 1000), 'request_id': request_id, 'app_type': app_type, 'mid': mid, 'uid': uid, 'operation': 'get_recommend_params', 'executeTime': (time.time() - param_st) * 1000 }) # 简单召回 - 排序 - 兜底 get_result_st = time.time() rank_result, last_rov_recall_key = video_recommend(request_id=request_id, mid=mid, uid=uid, app_type=app_type, size=size, top_K=top_K, flow_pool_P=flow_pool_P, algo_type='', client_info=client_info, ab_code=ab_code, expire_time=expire_time, rule_key=rule_key, data_key=data_key, no_op_flag=no_op_flag, old_video_index=old_video_index, video_id=video_id, params=params) log_.info({ 'logTimestamp': int(time.time() * 1000), 'request_id': request_id, 'app_type': app_type, 'mid': mid, 'uid': uid, 'operation': 'get_recommend_result', 'executeTime': (time.time() - get_result_st) * 1000 }) # ab-test # result = ab_test_op(rank_result=rank_result, # ab_code_list=[config_.AB_CODE['position_insert'], config_.AB_CODE['relevant_video_op']], # app_type=app_type, mid=mid, uid=uid, head_vid=video_id, size=size) # redis数据刷新 update_redis_st = time.time() update_redis_data(result=rank_result, app_type=app_type, mid=mid, last_rov_recall_key=last_rov_recall_key, top_K=top_K) log_.info({ 'logTimestamp': int(time.time() * 1000), 'request_id': request_id, 'app_type': app_type, 'mid': mid, 'uid': uid, 'operation': 'update_redis_data', 'executeTime': (time.time() - update_redis_st) * 1000 }) return rank_result def special_mid_recommend(request_id, mid, uid, app_type, size, ab_code=config_.AB_CODE['special_mid'], push_from=config_.PUSH_FROM['special_mid'], expire_time=24*3600): redis_helper = RedisHelper() # 特殊mid推荐指定视频列表 pool_recall = PoolRecall(request_id=request_id, app_type=app_type, mid=mid, uid=uid, ab_code=ab_code) # 获取相关redis key special_key_name, redis_date = pool_recall.get_pool_redis_key(pool_type='special') # 用户上一次在rov召回池对应的位置 last_special_recall_key = f'{config_.LAST_VIDEO_FROM_SPECIAL_POOL_PREFIX}{app_type}:{mid}:{redis_date}' value = redis_helper.get_data_from_redis(last_special_recall_key) if value: idx = redis_helper.get_index_with_data(special_key_name, value) if not idx: idx = 0 else: idx += 1 else: idx = 0 recall_result = [] # 每次获取的视频数 get_size = size * 5 # 记录获取频次 freq = 0 while len(recall_result) < size: freq += 1 if freq > config_.MAX_FREQ_FROM_ROV_POOL: break # 获取数据 data = redis_helper.get_data_zset_with_index(key_name=special_key_name, start=idx, end=idx + get_size - 1, with_scores=True) if not data: break # 获取视频id,并转换类型为int,并存储为key-value{videoId: score} # 添加视频源参数 pushFrom, abCode temp_result = [{'videoId': int(value[0]), 'rovScore': value[1], 'pushFrom': push_from, 'abCode': ab_code} for value in data] recall_result.extend(temp_result) idx += get_size # 将此次获取的末位视频id同步刷新到Redis中，方便下次快速定位到召回位置，过期时间为1天 if mid and recall_result: # mid为空时，不做记录 redis_helper.set_data_to_redis(key_name=last_special_recall_key, value=recall_result[:size][-1]['videoId'], expire_time=expire_time) return recall_result[:size] def get_special_mid_list(): redis_helper = RedisHelper() special_mid_list = redis_helper.get_data_from_set(key_name=config_.KEY_NAME_SPECIAL_MID) if special_mid_list: return special_mid_list else: return [] if __name__ == '__main__': videos = [ {"videoId": 10136461, "rovScore": 99.971, "pushFrom": "recall_pool", "abCode": 10000}, {"videoId": 10239014, "rovScore": 99.97, "pushFrom": "recall_pool", "abCode": 10000}, {"videoId": 9851154, "rovScore": 99.969, "pushFrom": "recall_pool", "abCode": 10000}, {"videoId": 10104347, "rovScore": 99.968, "pushFrom": "recall_pool", "abCode": 10000}, {"videoId": 10141507, "rovScore": 99.967, "pushFrom": "recall_pool", "abCode": 10000}, {"videoId": 10292817, "flowPool": "2#6#2#1641780979606", "rovScore": 53.926690610816486, "pushFrom": "flow_pool", "abCode": 10000}, {"videoId": 10224932, "flowPool": "2#5#1#1641800279644", "rovScore": 53.47890460059617, "pushFrom": "flow_pool", "abCode": 10000}, {"videoId": 9943255, "rovScore": 99.966, "pushFrom": "recall_pool", "abCode": 10000}, {"videoId": 10282970, "flowPool": "2#5#1#1641784814103", "rovScore": 52.682815076325575, "pushFrom": "flow_pool", "abCode": 10000}, {"videoId": 10282205, "rovScore": 99.965, "pushFrom": "recall_pool", "abCode": 10000} ] res = relevant_video_top_recommend(app_type=4, mid='', uid=1111, head_vid=123, videos=videos, size=10) print(res)