feat（新功能）: sketch 推荐算法

fix（修复bug）:
docs（文档变更）:
refactor（重构）:
test(增加测试):

app/api/api_recommendation.py

@@ -0,0 +1,118 @@
+import io
+import logging
+import sys
+import time
+from typing import List
+
+import numpy as np
+from apscheduler.schedulers.background import BackgroundScheduler
+from apscheduler.triggers.cron import CronTrigger
+from fastapi import HTTPException, APIRouter
+
+from app.service.recommend.service import load_resources, matrix_data
+
+sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
+logger = logging.getLogger()
+router = APIRouter()
+
+
+@router.on_event("startup")
+async def startup_event():
+    # 初始加载
+    load_resources()
+
+    # 配置定时任务
+    scheduler = BackgroundScheduler()
+    scheduler.add_job(
+        load_resources,
+        trigger=CronTrigger(hour=0, minute=30),
+        name="每日资源刷新"
+    )
+    scheduler.start()
+    logger.info("定时任务已启动")
+
+
+@router.get("/recommend/{user_id}/{category}/{num_recommendations}", response_model=List[str])
+async def get_recommendations(user_id: int, category: str, num_recommendations: int = 10):
+    """
+    :param user_id: 4
+    :param category: female_skirt
+    :param num_recommendations: 1
+    :return:
+            [
+                "aida-sys-image/images/female/skirt/903000017.jpg"
+            ]
+    """
+    try:
+        start_time = time.time()
+        cache_key = (user_id, category)
+
+        # 检查缓存
+        if cache_key in matrix_data["cached_scores"]:
+            processed_inter, processed_feat = matrix_data["cached_scores"][cache_key]
+            valid_sketch_idxs_inter = matrix_data["cached_valid_idxs"][cache_key]
+        else:
+            # 实时计算逻辑（同原代码）
+            user_idx_inter = matrix_data["user_index_interaction"].get(user_id)
+            user_idx_feature = matrix_data["user_index_feature"].get(user_id)
+
+            category_iids = matrix_data["category_to_iids"].get(category, [])
+            valid_sketch_idxs_inter = [
+                idx for iid, idx in matrix_data["sketch_index_interaction"].items()
+                if iid in category_iids
+            ]
+
+            # 处理交互分数
+            raw_inter_scores = []
+            if user_idx_inter is not None and valid_sketch_idxs_inter:
+                raw_inter_scores = matrix_data["interaction_matrix"][user_idx_inter, valid_sketch_idxs_inter]
+            processed_inter = raw_inter_scores * 0.7
+
+            # 处理特征分数
+            valid_sketch_idxs_feature = [
+                idx for iid, idx in matrix_data["sketch_index_feature"].items()
+                if iid in category_iids
+            ]
+            raw_feat_scores = []
+            if user_idx_feature is not None and valid_sketch_idxs_feature:
+                raw_feat_scores = matrix_data["feature_matrix"][user_idx_feature, valid_sketch_idxs_feature]
+                raw_feat_scores = (raw_feat_scores - np.min(raw_feat_scores)) / (
+                        np.max(raw_feat_scores) - np.min(raw_feat_scores) + 1e-8)
+                processed_feat = raw_feat_scores * 0.3
+            else:
+                processed_feat = np.array([])
+
+            # 更新缓存
+            matrix_data["cached_scores"][cache_key] = (processed_inter, processed_feat)
+            matrix_data["cached_valid_idxs"][cache_key] = valid_sketch_idxs_inter
+
+        # 合并分数
+        final_scores = processed_inter + processed_feat
+        valid_sketch_idxs = matrix_data["cached_valid_idxs"][cache_key]
+
+        # 概率采样
+        scores = np.array(final_scores)
+
+        # 调整后的概率转换（带温度控制的softmax）
+        def calibrated_softmax(scores, temperature=1.0):
+            scores = scores / temperature
+            scale = scores - max(scores)
+            exps = np.exp(scale)
+            return exps / np.sum(exps)
+
+        probs = calibrated_softmax(scores, 0.07)
+
+        chosen_indices = np.random.choice(
+            len(valid_sketch_idxs),
+            size=min(num_recommendations, len(valid_sketch_idxs)),
+            p=probs,
+            replace=False
+        )
+        recommendations = [matrix_data["iid_to_sketch"][valid_sketch_idxs[idx]] for idx in chosen_indices]
+
+        logger.info(f"推荐生成完成，耗时: {time.time() - start_time:.2f}秒")
+        return recommendations
+
+    except Exception as e:
+        logger.error(f"推荐失败: {str(e)}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))