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75 lines
3.1 KiB
75 lines
3.1 KiB
import json
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from datetime import datetime
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from config import logger
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from config import test_user_id
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from ranking.subject.user_recall_rank_features import init_rank_features
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from ranking.subject.user_recall_rank_features import build_rank_features_online
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from matching.subject.cold_start_recall import cold_start_user_recall
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from matching.subject.multi_recall_predict import multi_recall_predict
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init_rank_features()
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from ranking.subject.xdeepfm_ranker_predict import xdeepfm_ranker_predict
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from ranking.subject.bst_ranker_predict import bst_ranker_predict
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from ranking.subject.difm_ranker_predict import difm_ranker_predict
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def subject_recommend_online(user_id, disciplines_id_list=None, topk=10, rank_method='1'):
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"""
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根据用户ID获取推荐的实训ID列表
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1. 使用多路召回获取用户的实训ID列表
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2. 根据召回列表生成排序模型特征
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3. 用排序模型对召回列表重新排序
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4. 返回排序后的结果
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"""
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start_time = datetime.now()
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logger.info(f"本次需要进行推荐的用户ID: {user_id}")
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recommend_results = {}
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recommend_results.clear()
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# 1.先进行多路召回
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recommend_results, only_cold_start_recall = multi_recall_predict(user_id, disciplines_id_list, topk=topk)
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if only_cold_start_recall and (len(recommend_results) > 0):
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# 计算耗时毫秒
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end_time = datetime.utcnow()
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cost_time_millisecond = round(float((end_time - start_time).microseconds / 1000.0), 3)
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logger.info(f"本次推荐总耗时: {cost_time_millisecond} 毫秒")
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return recommend_results
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# 2.构建排序模型特征
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user_item_feats_df = build_rank_features_online(user_id, recommend_results)
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# 3.使用排序模型对召回的候选物品进行排序
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# 如果没有召回数据则根据兴趣标签使用冷启动召回推荐
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if user_item_feats_df.empty:
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recommend_results = cold_start_user_recall(disciplines_id_list, topk=topk)
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else:
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if topk > user_item_feats_df.shape[0]:
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topk = user_item_feats_df.shape[0]
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if rank_method == '0':
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rank_results = xdeepfm_ranker_predict(user_item_feats_df, topk=topk)
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elif rank_method == '1':
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rank_results = bst_ranker_predict(user_item_feats_df, topk=topk)
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elif rank_method == '2':
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rank_results = difm_ranker_predict(user_item_feats_df, topk=topk)
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recommend_results = dict(zip(rank_results['subject_id'], rank_results['subject_name']))
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# 计算耗时毫秒
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end_time = datetime.utcnow()
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cost_time_millisecond = round(float((end_time - start_time).microseconds / 1000.0), 3)
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logger.info(f"本次推荐总耗时: {cost_time_millisecond} 毫秒")
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return recommend_results
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if __name__ == '__main__':
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recommend_results = subject_recommend_online(user_id=test_user_id,
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disciplines_id_list=[],
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topk=10,
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rank_method='0')
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print(json.dumps(recommend_results, ensure_ascii=False, indent=4)) |