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143 lines
6.0 KiB
143 lines
6.0 KiB
from tqdm import tqdm
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import os
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import sys
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sys.path.append(os.getcwd())
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import warnings
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import pickle
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from config import logger
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from config import subject_item_embedding_recall_dict
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from config import subject_itemcf_recall_dict
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from config import subject_dssm_recall_dict
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from config import subject_dssm_usercf_recall_dict
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from config import subject_fm_recall_dict
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from config import subject_mind_recall0_dict
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from config import subject_mind_recall1_dict
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from config import subject_pinsage_recall_dict
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from config import subject_youtubednn_recall_dict
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from config import subject_youtubednn_usercf_recall_dict
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from config import subject_final_recall_items_dict
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tqdm.pandas()
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warnings.filterwarnings('ignore')
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def combine_recall_results(user_multi_recall_dict, weight_dict=None, topk=100, save_results=True):
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"""
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多路召回合并，将所有的召回策略得到的物品列表合并起来
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"""
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final_recall_items_dict = {}
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# 对每一种召回结果按照用户进行归一化，方便后面多种召回结果，相同用户的物品之间权重相加
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def norm_user_recall_items_sim(sorted_item_list):
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if len(sorted_item_list) < 2:
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return sorted_item_list
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min_sim = sorted_item_list[-1][1]
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max_sim = sorted_item_list[0][1]
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norm_sorted_item_list = []
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for item, score in sorted_item_list:
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if max_sim > 0:
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norm_score = 1.0 * (score - min_sim) / (max_sim - min_sim) if max_sim > min_sim else 1.0
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else:
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norm_score = 0.0
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norm_sorted_item_list.append((item, norm_score))
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return norm_sorted_item_list
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logger.info('开始多路召回合并...')
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for method, user_recall_items in tqdm(user_multi_recall_dict.items()):
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# 在计算最终召回结果的时候，也可以为每一种召回结果设置一个权重
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if weight_dict == None:
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recall_method_weight = 1
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else:
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recall_method_weight = weight_dict[method]
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for user_id, sorted_item_list in user_recall_items.items():
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# 进行归一化
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user_recall_items[user_id] = norm_user_recall_items_sim(sorted_item_list)
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for user_id, sorted_item_list in user_recall_items.items():
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final_recall_items_dict.setdefault(user_id, {})
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for item, score in sorted_item_list:
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final_recall_items_dict[user_id].setdefault(item, 0)
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final_recall_items_dict[user_id][item] += recall_method_weight * score
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final_recall_items_dict_rank = {}
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# 多路召回时可以控制最终的召回数量
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for user, recall_item_dict in final_recall_items_dict.items():
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final_recall_items_dict_rank[user] = sorted(recall_item_dict.items(),
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key = lambda x: x[1], reverse = True)[:topk]
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if save_results:
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logger.info('保存多路召回合并的结果')
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pickle.dump(final_recall_items_dict_rank, open(subject_final_recall_items_dict, 'wb'))
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return final_recall_items_dict_rank
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if __name__ == '__main__':
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# 定义多路召回的字典，将多路召回的结果都保存在这个字典当中
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user_multi_recall_dict = {'itemcf_recall': {},
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'item_embedding_recall': {},
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'dssm_recall':{},
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'dssm_usercf_recall':{},
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'fm_recall':{},
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'mind_recall0':{},
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'mind_recall1':{},
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'pinsage_recall':{},
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'youtubednn_recall': {},
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'youtubednn_usercf_recall': {}
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}
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logger.info('加载itemcf召回结果')
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user_multi_recall_dict['itemcf_recall'] = pickle.load(open(subject_itemcf_recall_dict, 'rb'))
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logger.info('加载item embedding召回结果')
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user_multi_recall_dict['item_embedding_recall'] = pickle.load(open(subject_item_embedding_recall_dict, 'rb'))
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logger.info('加载dssm召回结果')
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user_multi_recall_dict['dssm_recall'] = pickle.load(open(subject_dssm_recall_dict, 'rb'))
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logger.info('加载dssm usercf召回结果')
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user_multi_recall_dict['dssm_usercf_recall'] = pickle.load(open(subject_dssm_usercf_recall_dict, 'rb'))
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logger.info('加载fm召回结果')
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user_multi_recall_dict['fm_recall'] = pickle.load(open(subject_fm_recall_dict, 'rb'))
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logger.info('加载mind 用户兴趣1召回结果')
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user_multi_recall_dict['mind_recall0'] = pickle.load(open(subject_mind_recall0_dict, 'rb'))
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logger.info('加载mind 用户兴趣1召回结果')
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user_multi_recall_dict['mind_recall1'] = pickle.load(open(subject_mind_recall1_dict, 'rb'))
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logger.info('加载pinsage召回结果')
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user_multi_recall_dict['youtubednn_usercf_recall'] = pickle.load(open(subject_pinsage_recall_dict, 'rb'))
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logger.info('加载YoutubeDNN召回结果')
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user_multi_recall_dict['youtubednn_recall'] = pickle.load(open(subject_youtubednn_recall_dict, 'rb'))
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logger.info('加载youtube usercf召回结果')
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user_multi_recall_dict['youtubednn_usercf_recall'] = pickle.load(open(subject_youtubednn_usercf_recall_dict, 'rb'))
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# 每路召回的权重给不同的值，根据前面召回的情况调整参数的值
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weight_dict = {'itemcf_recall': 0.7,
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'item_embedding_recall': 1.5,
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'dssm_recall':2.8,
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'dssm_usercf_recall':3.9,
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'fm_recall':2.1,
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'mind_recall0':1.9,
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'mind_recall1':1.9,
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'pinsage_recall':8,
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'youtubednn_recall': 2.6,
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'youtubednn_usercf_recall': 3.9
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}
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# 最终合并之后每个用户召回300个物品进行排序
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final_recall_items_dict_rank = combine_recall_results(user_multi_recall_dict, weight_dict, topk=300) |