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81 lines
2.8 KiB
81 lines
2.8 KiB
5 months ago
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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 numpy as np
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import faiss
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import pickle
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from datetime import datetime
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from config import logger
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from config import embedding_dim
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from config import test_user_id
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from config import subject_mind_user_emb1_dict
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from config import subject_mind_user_embedding1_data
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from config import subject_mind_user1_faiss_model_path
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from config import subject_mind_user_embedding1_index_dict
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from config import subject_mind_item_emb_dict
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from config import subject_mind_item_embedding_data
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from config import subject_mind_item_faiss_model_path
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from config import subject_mind_item_embedding_index_dict
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logger.info('加载MIND召回数据')
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mind_user_emb_dict = pickle.load(open(subject_mind_user_emb1_dict, 'rb'))
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mind_user_embedding_data = pickle.load(open(subject_mind_user_embedding1_data, 'rb'))
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mind_user_embedding_index_dict = pickle.load(open(subject_mind_user_embedding1_index_dict, 'rb'))
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mind_item_emb_dict = pickle.load(open(subject_mind_item_emb_dict, 'rb'))
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mind_item_embedding_data = pickle.load(open(subject_mind_item_embedding_data, 'rb'))
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mind_item_embedding_index_dict = pickle.load(open(subject_mind_item_embedding_index_dict, 'rb'))
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logger.info('加载MIND召回模型')
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mind_user_faiss_model = faiss.read_index(subject_mind_user1_faiss_model_path)
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mind_item_faiss_model = faiss.read_index(subject_mind_item_faiss_model_path)
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def mind1_recall(user_id, topk=20):
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"""
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通过mind user1 embedding的相似度推荐学习伙伴
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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MIND召回的用户ID: {user_id}")
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recommend_results = {}
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recommend_results.clear()
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if (user_id not in mind_user_emb_dict):
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return recommend_results
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# 取出用户向量
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user_embs = mind_user_emb_dict[user_id]
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# reshape为二维
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user_embs = user_embs.reshape(-1, embedding_dim)
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# 找topk个相似的物品向量
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D, I = mind_item_faiss_model.search(np.ascontiguousarray(user_embs), topk + 1)
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top_k_index = list(I.ravel())
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top_k_sim = list(D.ravel())
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top_k_item = {}
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top_k_item.clear()
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# 还原相似向量索引对应的用户
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for i, index in enumerate(top_k_index):
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cur_user_id = mind_item_embedding_index_dict[index]
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# 过滤第一个最相似的用户是自己
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if cur_user_id != user_id:
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top_k_item[cur_user_id] = top_k_sim[i]
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top_k_item = sorted(top_k_item.items(), key=lambda x: x[1], reverse=True)[:topk]
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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 top_k_item
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if __name__ == '__main__':
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recall_results = mind1_recall(user_id=test_user_id, topk=20)
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print(recall_results)
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