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from datetime import datetime
import pickle
import json
from config import logger
from config import shixun_xdeepfm_rank_dict
from config import shixun_bst_rank_dict
from config import test_user_id
from matching.shixun.cold_start_recall import cold_start_user_recall
logger.info('加载实训推荐离线排序字典')
xdeepfm_rank_dict = pickle.load(open(shixun_xdeepfm_rank_dict, 'rb'))
bst_rank_dict = pickle.load(open(shixun_bst_rank_dict, 'rb'))
def shixun_recommend_offline(user_id, disciplines_id_list=None, topk=100, rank_method='1'):
"""
根据用户ID获取推荐的实训ID列表
直接从计算好的离线排序字典中取出数据返回
"""
start_time = datetime.now()
logger.info(f"本次需要进行推荐的用户ID: {user_id}")
has_data = False
rank_list = []
rank_list.clear()
recommend_results = {}
recommend_results.clear()
# 如果离线排序字典中有此用户则直接取出
if rank_method == '1':
if user_id in xdeepfm_rank_dict:
has_data = True
rank_list = xdeepfm_rank_dict[user_id]
else:
if user_id in bst_rank_dict:
has_data = True
rank_list = bst_rank_dict[user_id]
# 没有召回数据则根据兴趣标签使用冷启动召回推荐
if has_data == False:
recommend_results = cold_start_user_recall(user_id,disciplines_id_list, topk=topk)
else:
if topk > len(rank_list):
topk = len(rank_list)
# 取topk个返回
rank_list = rank_list[:topk]
recommend_results = {shixun_id: shixun_name for shixun_id, shixun_name in rank_list}
# 计算耗时毫秒
end_time = datetime.utcnow()
cost_time_millisecond = round(float((end_time - start_time).microseconds / 1000.0), 3)
logger.info(f"本次推荐总耗时: {cost_time_millisecond} 毫秒")
return recommend_results
if __name__ == '__main__':
recommend_results = shixun_recommend_offline(user_id=test_user_id,
disciplines_id_list=[1,2],
topk=100,
rank_method=1)
print(json.dumps(recommend_results, ensure_ascii=False, indent=4))