算法与视觉优化

backend/app/services/fetcher_service.py

@@ -26,9 +26,9 @@ SIMILARITY_THRESHOLD = float(os.getenv("SIMILARITY_THRESHOLD", 0.72))
 API_BASE_URL = os.getenv("API_BASE_URL", "https://newsnow.busiyi.world/api/s")
 EMBEDDING_MODEL_PATH = os.getenv("EMBEDDING_MODEL_PATH", "")
 
-print("正在加载向量模型...")
+print("正在加载 BAAI/bge-m3 向量模型...")
 # 全局单例
-embedder_model = SentenceTransformer(EMBEDDING_MODEL_PATH, local_files_only=True)
+embedder_model = SentenceTransformer(EMBEDDING_MODEL_PATH, local_files_only=True, device="cuda")
 print("模型加载完成。")
 
 

backend/app/services/matching_service.py

@@ -1,6 +1,6 @@
 """
 匹配服务：根据用户兴趣关键词（精确 + 语义）推荐事件
-打分融合：匹配分 + 标签相关度 + 热度 + 新鲜度加成
+打分融合：标签/标题匹配分 + 标签相关度 + 热度 + 新鲜度加成
 """
 import os
 from dataclasses import dataclass
@@ -14,7 +14,7 @@ from app.models.models import ExtractedTopic, TargetType, UnifiedEvent, UserTopi
 from app.services.fetcher_service import embedder_model
 
 
-# 语义匹配阈值：用户关键词和事件标签向量相似度达到该值才计入语义命中
+# 语义匹配阈值：用户关键词和事件标签/标题向量相似度达到该值才计入语义命中
 DEFAULT_PREFERENCE_SEMANTIC_THRESHOLD = 0.78
 PREFERENCE_SEMANTIC_THRESHOLD = float(
     os.getenv("PREFERENCE_SEMANTIC_THRESHOLD", str(DEFAULT_PREFERENCE_SEMANTIC_THRESHOLD))
@@ -41,6 +41,31 @@ def _normalize_text(text: str) -> str:
     return text.strip().casefold()
 
 
+def _find_exact_preference_match(
+    target_text: str,
+    normalized_preferences: list[tuple[str, str]],
+) -> str | None:
+    """
+    判断目标文本是否与某个用户兴趣词形成“精确命中”。
+    命中条件：
+    1. 标准化后完全相等
+    2. 二者互为包含关系
+    返回命中的原始兴趣词，未命中则返回 None。
+    """
+    normalized_target = _normalize_text(target_text)
+    if not normalized_target:
+        return None
+
+    for raw_pref, normalized_pref in normalized_preferences:
+        if not normalized_pref:
+            continue
+        if normalized_target == normalized_pref:
+            return raw_pref
+        if normalized_pref in normalized_target or normalized_target in normalized_pref:
+            return raw_pref
+    return None
+
+
 _EMBEDDING_CACHE: dict[str, np.ndarray] = {}
 MAX_CACHE_SIZE = 10000
 
@@ -86,6 +111,26 @@ def _build_keyword_embedding_map(keywords: list[str]) -> dict[str, np.ndarray]:
     return result
 
 
+def _find_best_semantic_match(
+    target_text: str,
+    target_vec_map: dict[str, np.ndarray],
+    pref_vec_map: dict[str, np.ndarray],
+) -> tuple[str | None, float]:
+    """返回与目标文本最接近的兴趣词及其余弦相似度。"""
+    target_vec = target_vec_map.get(target_text)
+    if target_vec is None:
+        return None, -1.0
+
+    best_pref = None
+    best_sim = -1.0
+    for pref_keyword, pref_vec in pref_vec_map.items():
+        sim = float(np.dot(target_vec, pref_vec))
+        if sim > best_sim:
+            best_sim = sim
+            best_pref = pref_keyword
+    return best_pref, best_sim
+
+
 def _ensure_aware(dt: datetime) -> datetime:
     """SQLite 读出的 datetime 不带时区信息，统一补上 UTC 后才能和 utcnow() 做减法。"""
     if dt.tzinfo is None:
@@ -116,8 +161,8 @@ def recommend_events_for_user(
 ) -> list[MatchedEventResult]:
     """
     用户兴趣推荐主流程：
-    1) 精确匹配：用户词 == EVENT 标签
+    1) 精确匹配：用户词 vs EVENT 标签/标题
-    2) 语义匹配：用户词向量 vs EVENT 标签向量（超过阈值）
+    2) 语义匹配：用户词向量 vs EVENT 标签/标题向量（超过阈值）
     3) 打分融合：匹配分 + 标签相关度 + 热度 + 新鲜度
     """
     final_limit = max(1, min(limit, PREFERENCE_RECOMMEND_MAX_LIMIT))
@@ -167,8 +212,6 @@ def recommend_events_for_user(
         )
         .all()
     )
-    if not topic_rows:
-        return []
 
     # 组织事件标签映射：event_id -> [(tag, relevance_score), ...]
     event_topics: dict[int, list[tuple[str, float | None]]] = {}
@@ -177,10 +220,6 @@ def recommend_events_for_user(
             continue
         event_topics.setdefault(event_id, []).append((topic_keyword, relevance_score))
 
-    # 如果某事件没有标签，就不参与推荐
-    if not event_topics:
-        return []
-
     # 3. 批量编码用户词与标签词，减少模型调用次数
     unique_preference_keywords = list(dict.fromkeys(preference_keywords))
     unique_topic_keywords = list(dict.fromkeys([row[1] for row in topic_rows if row[1]]))
@@ -188,13 +227,21 @@ def recommend_events_for_user(
     topic_vec_map = _build_keyword_embedding_map(unique_topic_keywords)
 
     # 预先建立“标准化后用户词集合”，用于精确匹配
-    normalized_pref_set = {_normalize_text(word) for word in unique_preference_keywords}
+    normalized_preference_pairs = [
+        (word, _normalize_text(word))
+        for word in unique_preference_keywords
+        if _normalize_text(word)
+    ]
+    unique_event_titles = list(
+        dict.fromkeys(
+            [event.unified_title.strip() for event in events if event.unified_title and event.unified_title.strip()]
+        )
+    )
+    title_vec_map = _build_keyword_embedding_map(unique_event_titles)
 
     scored_results: list[MatchedEventResult] = []
     for event in events:
         topic_list = event_topics.get(event.id, [])
-        if not topic_list:
-            continue
 
         exact_hits: list[str] = []
         semantic_hits: list[dict[str, Any]] = []
@@ -202,37 +249,18 @@ def recommend_events_for_user(
 
         # 对每个事件标签做精确匹配或语义匹配
         for topic_keyword, topic_relevance in topic_list:
-            normalized_topic = _normalize_text(topic_keyword)
             topic_relevance_score = float(topic_relevance) if topic_relevance is not None else 50.0
 
             # 1) 精确命中（包括完全相等与包含关系）
-            matched_exact = False
+            matched_pref = _find_exact_preference_match(topic_keyword, normalized_preference_pairs)
-            if normalized_topic in normalized_pref_set:
+            if matched_pref is not None:
-                matched_exact = True
-            else:
-                for pref_word in normalized_pref_set:
-                    if pref_word and (pref_word in normalized_topic or normalized_topic in pref_word):
-                        matched_exact = True
-                        break
-
-            if matched_exact:
                 exact_hits.append(topic_keyword)
                 # 精确命中给较高基础分，标签自身相关度作为增益
                 score += 45.0 + topic_relevance_score * 0.2
                 continue
 
             # 2) 语义命中（未精确命中时再算）
-            topic_vec = topic_vec_map.get(topic_keyword)
+            best_pref, best_sim = _find_best_semantic_match(topic_keyword, topic_vec_map, pref_vec_map)
-            if topic_vec is None:
-                continue
-
-            best_pref = None
-            best_sim = -1.0
-            for pref_keyword, pref_vec in pref_vec_map.items():
-                sim = float(np.dot(topic_vec, pref_vec))
-                if sim > best_sim:
-                    best_sim = sim
-                    best_pref = pref_keyword
 
             if best_pref is not None and best_sim >= similarity_threshold:
                 semantic_hits.append(
@@ -245,6 +273,25 @@ def recommend_events_for_user(
                 # 语义命中分略低于精确命中，并由相似度放大
                 score += best_sim * 35.0 + topic_relevance_score * 0.12
 
+        # 标题也参与匹配，但权重低于结构化标签，避免长标题过度主导排序。
+        event_title = (event.unified_title or "").strip()
+        if event_title:
+            title_exact_pref = _find_exact_preference_match(event_title, normalized_preference_pairs)
+            if title_exact_pref is not None:
+                exact_hits.append(f"标题:{title_exact_pref}")
+                score += 30.0
+            else:
+                best_pref, best_sim = _find_best_semantic_match(event_title, title_vec_map, pref_vec_map)
+                if best_pref is not None and best_sim >= similarity_threshold:
+                    semantic_hits.append(
+                        {
+                            "preference_keyword": best_pref,
+                            "topic_keyword": f"标题:{best_pref}",
+                            "similarity": round(best_sim, 4),
+                        }
+                    )
+                    score += best_sim * 24.0
+
         # 如果精确和语义都没命中，直接跳过
         if not exact_hits and not semantic_hits:
             continue

frontend/src/components/UnifiedEventCard.vue

@@ -111,6 +111,14 @@ function getRankingChartOptions(history: number[], platformColor: string) {
       height: 56,
       sparkline: { enabled: true },
       animations: { enabled: true, easing: 'easeinout' as const, speed: 400 },
+      events: {
+        mounted: (chartContext: any) => {
+          chartContext.el?.querySelector('.apexcharts-svg > title')?.remove()
+        },
+        updated: (chartContext: any) => {
+          chartContext.el?.querySelector('.apexcharts-svg > title')?.remove()
+        }
+      }
     },
     stroke: { curve: 'smooth' as const, width: 2 },
     fill: {

frontend/src/views/DashboardView.vue

@@ -182,6 +182,14 @@ function getRankingChartOptions(history: number[], platformColor: string) {
       height: 56,
       sparkline: { enabled: true },
       animations: { enabled: true, easing: 'easeinout' as const, speed: 400 },
+      events: {
+        mounted: (chartContext: any) => {
+          chartContext.el?.querySelector('.apexcharts-svg > title')?.remove()
+        },
+        updated: (chartContext: any) => {
+          chartContext.el?.querySelector('.apexcharts-svg > title')?.remove()
+        }
+      }
     },
     stroke: { curve: 'smooth' as const, width: 2 },
     fill: {

frontend/src/views/SearchView.vue

@@ -72,6 +72,12 @@ const chartOptions = ref<ApexOptions>({
     },
     // 点击图表数据点：切换选中时间，再次点击则取消筛选
     events: {
+      mounted: (chartContext: any) => {
+        chartContext.el?.querySelector('.apexcharts-svg > title')?.remove()
+      },
+      updated: (chartContext: any) => {
+        chartContext.el?.querySelector('.apexcharts-svg > title')?.remove()
+      },
       markerClick: function(event: unknown, chartContext: unknown, { dataPointIndex }: never) {
         if (searchResult.value && searchResult.value.timeline[dataPointIndex]) {
           const clickedTime = searchResult.value.timeline[dataPointIndex].time_label
@@ -585,7 +591,12 @@ async function handleSearch() {
 
 .chart-container {
   margin-top: 16px;
-  margin-left: -10px; /* 视觉上抵消 apexcharts 的默认左侧留白。 */
+  margin-left: -10px; 
+}
+
+.chart-container :deep(svg),
+.chart-container :deep(canvas) {
+  outline: none;
 }
 
 .events-section {
@@ -595,7 +606,6 @@ async function handleSearch() {
 .events-grid {
   display: flex;
   flex-direction: column;
-  /* 与 DashboardView 保持一致，列表按纵向堆叠展示。 */
 }
 
 .loading-state {

frontend/src/views/TopicsView.vue

@@ -156,7 +156,7 @@ onMounted(async () => {
             v-model="newKeyword"
             type="text"
             class="keyword-input"
-            placeholder="输入关键词，如「直升机」「科比」「佐巴扬」..."
+            placeholder="输入关键词，如「篮球」「科比」「科技」..."
             maxlength="100"
             @keydown="onInputKeydown"
           />