calculate_cosine_similarity.py

from pymongo import MongoClient
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

client = MongoClient("mongodb+srv://waseoke:[email protected]/test?retryWrites=true&w=majority")
db = client["two_tower_model"]
user_embedding_collection = db["user_embeddings"]
train_dataset = db["train_dataset"]

def calculate_similarity(user_id):
    # 사용자 임베딩 가져오기
    user_data = user_embedding_collection.find_one({"user_id": user_id})
    if not user_data:
        raise ValueError(f"No embedding found for user_id: {user_id}")
    
    user_embedding = np.array(user_data["embedding"]).reshape(1, -1)

    # Anchor 데이터 가져오기
    anchor_embeddings = []
    train_data = list(train_dataset.find())
    for entry in train_data:
        anchor_embeddings.append(entry["anchor_embedding"])
    
    anchor_embeddings = np.array(anchor_embeddings)

    # Cosine Similarity 계산
    similarities = cosine_similarity(user_embedding, anchor_embeddings).flatten()

    # 가장 유사한 anchor 선택
    most_similar_index = np.argmax(similarities)
    most_similar_entry = train_data[most_similar_index]
    most_similar_positive = most_similar_entry["positive"]

    print(f"Most similar anchor for user {user_id}: {most_similar_entry['anchor']}")
    print(f"Recommended positive product: {most_similar_positive}")
    return most_similar_positive