TASK:冷启动热度推荐回退；

app/api/api_recommendation.py

@@ -40,34 +40,44 @@ def softmax(scores):
     sum_exp = sum(exp_scores)
     return [s / sum_exp for s in exp_scores]
 
+# def get_random_recommendations(category: str, num: int) -> List[str]:
+#     """根据预加载热度向量推荐（冷启动）"""
+#     try:
+#         heat_data = matrix_data.get("heat_data", {})
+#
+#         if category not in heat_data:
+#             raise ValueError(f"热度数据缺少类别 {category}，使用随机推荐")
+#
+#         heat_dict = heat_data[category]  # {url: score}
+#         urls = list(heat_dict.keys())
+#         scores = list(heat_dict.values())
+#
+#         if not urls:
+#             raise ValueError("该类别下无热度记录，使用随机推荐")
+#
+#         probs = softmax(scores)
+#         sample_size = min(num, len(urls))
+#         sampled_urls = random.choices(urls, weights=probs, k=sample_size)
+#
+#         return sampled_urls
+#
+#     except Exception as e:
+#         # 回退：完全随机推荐
+#         all_iids = list(matrix_data["iid_to_sketch"].keys())
+#         category_iids = matrix_data["category_to_iids"].get(category, all_iids)
+#         sample_size = min(num, len(category_iids))
+#         sampled = np.random.choice(category_iids, size=sample_size, replace=False)
+#         return [matrix_data["iid_to_sketch"][iid] for iid in sampled]
+
 def get_random_recommendations(category: str, num: int) -> List[str]:
-    """根据预加载热度向量推荐（冷启动）"""
-    try:
-        heat_data = matrix_data.get("heat_data", {})
-
-        if category not in heat_data:
-            raise ValueError(f"热度数据缺少类别 {category}，使用随机推荐")
-
-        heat_dict = heat_data[category]  # {url: score}
-        urls = list(heat_dict.keys())
-        scores = list(heat_dict.values())
-
-        if not urls:
-            raise ValueError("该类别下无热度记录，使用随机推荐")
-
-        probs = softmax(scores)
-        sample_size = min(num, len(urls))
-        sampled_urls = random.choices(urls, weights=probs, k=sample_size)
-
-        return sampled_urls
-
-    except Exception as e:
-        # 回退：完全随机推荐
-        all_iids = list(matrix_data["iid_to_sketch"].keys())
-        category_iids = matrix_data["category_to_iids"].get(category, all_iids)
-        sample_size = min(num, len(category_iids))
-        sampled = np.random.choice(category_iids, size=sample_size, replace=False)
-        return [matrix_data["iid_to_sketch"][iid] for iid in sampled]
+    """全品类随机推荐"""
+    all_iids = list(matrix_data["iid_to_sketch"].keys())
+    # 优先从当前品类选择
+    category_iids = matrix_data["category_to_iids"].get(category, all_iids)
+    # 确保不超出实际数量
+    sample_size = min(num, len(category_iids))
+    sampled = np.random.choice(category_iids, size=sample_size, replace=False)
+    return [matrix_data["iid_to_sketch"][iid] for iid in sampled]
 
 
 @router.get("/recommend/{user_id}/{category}/{num_recommendations}/{brand_id}/{brand_scale}", response_model=List[str])