194 lines
7.5 KiB
Python
194 lines
7.5 KiB
Python
import io
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import logging
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import sys
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import time
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from typing import List
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import os
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import json
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import math
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import random
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import numpy as np
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from apscheduler.schedulers.background import BackgroundScheduler
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from apscheduler.triggers.cron import CronTrigger
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from fastapi import HTTPException, APIRouter
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from app.service.recommend.service import load_resources, matrix_data
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
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logger = logging.getLogger()
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router = APIRouter()
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@router.on_event("startup")
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async def startup_event():
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# 初始加载
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load_resources()
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# 配置定时任务
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scheduler = BackgroundScheduler()
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scheduler.add_job(
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load_resources,
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trigger=CronTrigger(hour=0, minute=30),
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name="每日资源刷新"
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)
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scheduler.start()
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logger.info("定时任务已启动")
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def softmax(scores):
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max_score = max(scores)
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exp_scores = [math.exp(s - max_score) for s in scores]
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sum_exp = sum(exp_scores)
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return [s / sum_exp for s in exp_scores]
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def get_random_recommendations(category: str, num: int) -> List[str]:
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"""根据预加载热度向量推荐（冷启动）"""
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try:
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heat_data = matrix_data.get("heat_data", {})
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if category not in heat_data:
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raise ValueError(f"热度数据缺少类别 {category}，使用随机推荐")
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heat_dict = heat_data[category] # {url: score}
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urls = list(heat_dict.keys())
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scores = list(heat_dict.values())
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if not urls:
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raise ValueError("该类别下无热度记录，使用随机推荐")
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probs = softmax(scores)
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sample_size = min(num, len(urls))
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sampled_urls = random.choices(urls, weights=probs, k=sample_size)
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return sampled_urls
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except Exception as e:
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# 回退：完全随机推荐
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all_iids = list(matrix_data["iid_to_sketch"].keys())
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category_iids = matrix_data["category_to_iids"].get(category, all_iids)
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sample_size = min(num, len(category_iids))
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sampled = np.random.choice(category_iids, size=sample_size, replace=False)
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return [matrix_data["iid_to_sketch"][iid] for iid in sampled]
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@router.get("/recommend/{user_id}/{category}/{num_recommendations}/{brand_id}/{brand_scale}", response_model=List[str])
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async def get_recommendations(user_id: int, category: str, brand_id: int, brand_scale: float, num_recommendations: int = 10):
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"""
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:param user_id: 4
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:param category: female_skirt
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:param num_recommendations: 1
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:return:
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[
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"aida-sys-image/images/female/skirt/903000017.jpg"
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]
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"""
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try:
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start_time = time.time()
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cache_key = (user_id, category)
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# === 新增：用户存在性检查 ===
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user_exists_inter = user_id in matrix_data["user_index_interaction"]
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user_exists_feat = user_id in matrix_data["user_index_feature"]
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# 任一矩阵不存在用户则返回随机推荐
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if not (user_exists_inter and user_exists_feat):
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logger.info(f"用户 {user_id} 数据不完整，触发随机推荐")
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return get_random_recommendations(category, num_recommendations)
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# 检查缓存
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if cache_key in matrix_data["cached_scores"]:
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processed_inter, processed_feat = matrix_data["cached_scores"][cache_key]
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valid_sketch_idxs_inter = matrix_data["cached_valid_idxs"][cache_key]
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else:
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# 实时计算逻辑（同原代码）
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user_idx_inter = matrix_data["user_index_interaction"].get(user_id)
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user_idx_feature = matrix_data["user_index_feature"].get(user_id)
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category_iids = matrix_data["category_to_iids"].get(category, [])
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valid_sketch_idxs_inter = [
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idx for iid, idx in matrix_data["sketch_index_interaction"].items()
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if iid in category_iids
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]
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# 处理交互分数
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raw_inter_scores = []
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if user_idx_inter is not None and valid_sketch_idxs_inter:
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raw_inter_scores = matrix_data["interaction_matrix"][user_idx_inter, valid_sketch_idxs_inter]
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processed_inter = raw_inter_scores * 0.7
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# 处理特征分数
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valid_sketch_idxs_feature = [
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idx for iid, idx in matrix_data["sketch_index_feature"].items()
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if iid in category_iids
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]
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raw_feat_scores = []
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if user_idx_feature is not None and valid_sketch_idxs_feature:
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raw_feat_scores = matrix_data["feature_matrix"][user_idx_feature, valid_sketch_idxs_feature]
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raw_feat_scores = (raw_feat_scores - np.min(raw_feat_scores)) / (
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np.max(raw_feat_scores) - np.min(raw_feat_scores) + 1e-8)
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processed_feat = raw_feat_scores
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else:
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processed_feat = np.array([])
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# 更新缓存
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matrix_data["cached_scores"][cache_key] = (processed_inter, processed_feat)
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matrix_data["cached_valid_idxs"][cache_key] = valid_sketch_idxs_inter
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# 合并分数
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if brand_id is not None:
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brand_idx_feature = matrix_data["brand_index_map"].get(brand_id)
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brand_feat_valid = (
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matrix_data["brand_feature_matrix"].size > 0 and # 矩阵非空
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brand_idx_feature is not None and
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valid_sketch_idxs_feature # 有可用索引
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)
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if brand_feat_valid:
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raw_brand_feat_scores = matrix_data["brand_feature_matrix"][
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brand_idx_feature, valid_sketch_idxs_feature
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]
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raw_brand_feat_scores = (raw_brand_feat_scores - np.min(raw_brand_feat_scores)) / (
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np.max(raw_brand_feat_scores) - np.min(raw_brand_feat_scores) + 1e-8
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)
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processed_brand_feat = raw_brand_feat_scores
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# 如果 processed_feat 是空的，替换为全 0，避免 shape 不一致
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if processed_feat.size == 0:
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processed_feat = np.zeros_like(processed_brand_feat)
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final_scores = processed_inter + 0.3 * (
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(1 - brand_scale) * processed_feat + brand_scale * processed_brand_feat
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)
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else:
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# brand 信息不可用
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final_scores = processed_inter + 0.3 * processed_feat if processed_feat.size > 0 else processed_inter
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else:
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final_scores = processed_inter + 0.3 * processed_feat if processed_feat.size > 0 else processed_inter
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valid_sketch_idxs = matrix_data["cached_valid_idxs"][cache_key]
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# 概率采样
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scores = np.array(final_scores)
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# 调整后的概率转换（带温度控制的softmax）
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def calibrated_softmax(scores, temperature=1.0):
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scores = scores / temperature
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scale = scores - max(scores)
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exps = np.exp(scale)
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return exps / np.sum(exps)
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probs = calibrated_softmax(scores, 0.09)
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chosen_indices = np.random.choice(
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len(valid_sketch_idxs),
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size=min(num_recommendations, len(valid_sketch_idxs)),
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p=probs,
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replace=False
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)
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recommendations = [matrix_data["iid_to_sketch"][valid_sketch_idxs[idx]] for idx in chosen_indices]
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logger.info(f"推荐生成完成，耗时: {time.time() - start_time:.2f}秒")
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return recommendations
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except Exception as e:
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logger.error(f"推荐失败: {str(e)}", exc_info=True)
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raise HTTPException(status_code=500, detail=str(e)) |