mirror of
https://github.com/stardrophere/InsightRadar.git
synced 2026-06-06 00:57:51 +08:00
stardrophere
273 lines
9.5 KiB
Python
273 lines
9.5 KiB
Python
"""
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匹配服务：根据用户兴趣关键词（精确 + 语义）推荐事件
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打分融合：匹配分 + 标签相关度 + 热度 + 新鲜度加成
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"""
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import os
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from dataclasses import dataclass
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from datetime import datetime, timedelta, timezone
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from typing import Any
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import numpy as np
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from sqlalchemy.orm import Session
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from app.models.models import ExtractedTopic, TargetType, UnifiedEvent, UserTopicPreference, utcnow
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from app.services.fetcher_service import embedder_model
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# 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
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DEFAULT_PREFERENCE_SEMANTIC_THRESHOLD = 0.78
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PREFERENCE_SEMANTIC_THRESHOLD = float(
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os.getenv("PREFERENCE_SEMANTIC_THRESHOLD", str(DEFAULT_PREFERENCE_SEMANTIC_THRESHOLD))
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)
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# 推荐列表最大返回条数
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DEFAULT_PREFERENCE_RECOMMEND_MAX_LIMIT = 50
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PREFERENCE_RECOMMEND_MAX_LIMIT = int(
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os.getenv("PREFERENCE_RECOMMEND_MAX_LIMIT", str(DEFAULT_PREFERENCE_RECOMMEND_MAX_LIMIT))
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)
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@dataclass
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class MatchedEventResult:
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"""用户兴趣匹配后的事件结果。"""
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event: UnifiedEvent
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match_score: float
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exact_hits: list[str]
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semantic_hits: list[dict[str, Any]]
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tags: list[str]
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def _normalize_text(text: str) -> str:
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"""统一小写与首尾空白，便于做稳定匹配。"""
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return text.strip().casefold()
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_EMBEDDING_CACHE: dict[str, np.ndarray] = {}
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MAX_CACHE_SIZE = 10000
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def _build_keyword_embedding_map(keywords: list[str]) -> dict[str, np.ndarray]:
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"""
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批量生成或从缓存获取关键词向量，并返回原词到向量的映射。
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结合了批量推理（Batching）的极速优势和内存缓存的 O(1) 读取优势。
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"""
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result: dict[str, np.ndarray] = {}
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if not keywords:
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return result
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uncached_keywords = []
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# 1. 尝试从缓存获取
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for keyword in keywords:
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if not keyword:
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continue
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if keyword in _EMBEDDING_CACHE:
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result[keyword] = _EMBEDDING_CACHE[keyword]
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else:
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uncached_keywords.append(keyword)
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# 2. 对未命中的词进行统一的批量推理
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if uncached_keywords:
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# 去重，避免同一个未缓存的词被计算多次
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unique_uncached = list(dict.fromkeys(uncached_keywords))
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vectors = embedder_model.encode(unique_uncached, normalize_embeddings=True, show_progress_bar=False)
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# 防止缓存无限增长：超过阈值时清空最早存入的一半（简单粗暴的内存控制）
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if len(_EMBEDDING_CACHE) > MAX_CACHE_SIZE:
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keys_to_delete = list(_EMBEDDING_CACHE.keys())[: MAX_CACHE_SIZE // 2]
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for k in keys_to_delete:
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del _EMBEDDING_CACHE[k]
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# 3. 将新计算的向量存入缓存并回填结果
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for keyword, vec in zip(unique_uncached, vectors):
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vec_array = np.asarray(vec, dtype=np.float32)
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_EMBEDDING_CACHE[keyword] = vec_array
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result[keyword] = vec_array
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return result
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def _ensure_aware(dt: datetime) -> datetime:
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"""SQLite 读出的 datetime 不带时区信息，统一补上 UTC 后才能和 utcnow() 做减法。"""
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if dt.tzinfo is None:
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return dt.replace(tzinfo=timezone.utc)
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return dt
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def _calc_freshness_bonus(event: UnifiedEvent) -> float:
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"""根据事件新鲜度给一个小额加分，避免旧热点长期占据推荐位。"""
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age_hours = max((utcnow() - _ensure_aware(event.created_at)).total_seconds() / 3600.0, 0.0)
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if age_hours <= 6:
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return 12.0
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if age_hours <= 24:
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return 8.0
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if age_hours <= 72:
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return 4.0
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return 0.0
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def recommend_events_for_user(
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db: Session,
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*,
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user_id: int,
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min_hot: int = 3,
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hours: int = 72,
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limit: int = 20,
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semantic_threshold: float | None = None,
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) -> list[MatchedEventResult]:
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"""
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用户兴趣推荐主流程：
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1) 精确匹配：用户词 == EVENT 标签
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2) 语义匹配：用户词向量 vs EVENT 标签向量（超过阈值）
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3) 打分融合：匹配分 + 标签相关度 + 热度 + 新鲜度
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"""
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final_limit = max(1, min(limit, PREFERENCE_RECOMMEND_MAX_LIMIT))
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similarity_threshold = (
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semantic_threshold
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if semantic_threshold is not None
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else PREFERENCE_SEMANTIC_THRESHOLD
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)
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# 1. 读取用户兴趣词
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preferences = (
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db.query(UserTopicPreference)
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.filter(UserTopicPreference.user_id == user_id)
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.all()
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)
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if not preferences:
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return []
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preference_keywords = [pref.interested_keyword.strip() for pref in preferences if pref.interested_keyword.strip()]
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if not preference_keywords:
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return []
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# 2. 读取候选事件（时间 + 热度过滤，避免全表扫描）
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time_limit = utcnow() - timedelta(hours=hours)
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events = (
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db.query(UnifiedEvent)
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.filter(
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UnifiedEvent.hot_score >= min_hot,
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UnifiedEvent.created_at >= time_limit,
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)
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.order_by(UnifiedEvent.hot_score.desc(), UnifiedEvent.created_at.desc())
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.all()
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)
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if not events:
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return []
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event_id_list = [event.id for event in events]
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topic_rows = (
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db.query(
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ExtractedTopic.target_id,
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ExtractedTopic.topic_keyword,
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ExtractedTopic.relevance_score,
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)
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.filter(
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ExtractedTopic.target_type == TargetType.EVENT,
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ExtractedTopic.target_id.in_(event_id_list),
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)
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.all()
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)
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if not topic_rows:
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return []
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# 组织事件标签映射：event_id -> [(tag, relevance_score), ...]
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event_topics: dict[int, list[tuple[str, float | None]]] = {}
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for event_id, topic_keyword, relevance_score in topic_rows:
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if not topic_keyword:
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continue
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event_topics.setdefault(event_id, []).append((topic_keyword, relevance_score))
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# 如果某事件没有标签，就不参与推荐
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if not event_topics:
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return []
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# 3. 批量编码用户词与标签词，减少模型调用次数
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unique_preference_keywords = list(dict.fromkeys(preference_keywords))
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unique_topic_keywords = list(dict.fromkeys([row[1] for row in topic_rows if row[1]]))
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pref_vec_map = _build_keyword_embedding_map(unique_preference_keywords)
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topic_vec_map = _build_keyword_embedding_map(unique_topic_keywords)
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# 预先建立“标准化后用户词集合”，用于精确匹配
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normalized_pref_set = {_normalize_text(word) for word in unique_preference_keywords}
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scored_results: list[MatchedEventResult] = []
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for event in events:
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topic_list = event_topics.get(event.id, [])
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if not topic_list:
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continue
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exact_hits: list[str] = []
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semantic_hits: list[dict[str, Any]] = []
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score = 0.0
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# 对每个事件标签做精确匹配或语义匹配
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for topic_keyword, topic_relevance in topic_list:
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normalized_topic = _normalize_text(topic_keyword)
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topic_relevance_score = float(topic_relevance) if topic_relevance is not None else 50.0
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# 1) 精确命中（包括完全相等与包含关系）
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matched_exact = False
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if normalized_topic in normalized_pref_set:
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matched_exact = True
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else:
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for pref_word in normalized_pref_set:
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if pref_word and (pref_word in normalized_topic or normalized_topic in pref_word):
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matched_exact = True
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break
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if matched_exact:
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exact_hits.append(topic_keyword)
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# 精确命中给较高基础分，标签自身相关度作为增益
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score += 45.0 + topic_relevance_score * 0.2
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continue
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# 2) 语义命中（未精确命中时再算）
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topic_vec = topic_vec_map.get(topic_keyword)
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if topic_vec is None:
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continue
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best_pref = None
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best_sim = -1.0
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for pref_keyword, pref_vec in pref_vec_map.items():
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sim = float(np.dot(topic_vec, pref_vec))
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if sim > best_sim:
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best_sim = sim
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best_pref = pref_keyword
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if best_pref is not None and best_sim >= similarity_threshold:
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semantic_hits.append(
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{
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"preference_keyword": best_pref,
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"topic_keyword": topic_keyword,
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"similarity": round(best_sim, 4),
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}
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)
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# 语义命中分略低于精确命中，并由相似度放大
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score += best_sim * 35.0 + topic_relevance_score * 0.12
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# 如果精确和语义都没命中，直接跳过
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if not exact_hits and not semantic_hits:
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continue
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# 融合事件热度和新鲜度，避免只看语义分
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score += min(event.hot_score, 100) * 0.3
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score += _calc_freshness_bonus(event)
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# 返回标签时做去重，保证接口稳定
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tags = list(dict.fromkeys([item[0] for item in topic_list]))
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scored_results.append(
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MatchedEventResult(
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event=event,
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match_score=round(score, 2),
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exact_hits=list(dict.fromkeys(exact_hits)),
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semantic_hits=semantic_hits,
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tags=tags,
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)
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)
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scored_results.sort(
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key=lambda item: (item.match_score, item.event.hot_score, item.event.created_at),
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reverse=True,
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)
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return scored_results[:final_limit]
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