新功能：个性化推荐算法

server/tests/test_reco_engine.py

@@ -0,0 +1,173 @@
+from __future__ import annotations
+
+from datetime import datetime, timezone
+
+import pytest
+
+from app.features.personalized_reco.content_repository.interface import ContentRepository
+from app.features.personalized_reco.content_repository.types import ContentProfileDTO
+from app.features.personalized_reco.reco_engine.orchestrator import recommend
+from app.features.personalized_reco.reco_engine.types import RecoConstraints
+from app.features.user_profile_scoring.types import ProfileAnswered, UserProfileV1_2, UserStageOneHot
+
+
+class _FakeRepo(ContentRepository):
+    def __init__(self, candidates_by_level: dict[int, list[ContentProfileDTO]]):
+        self._candidates_by_level = candidates_by_level
+
+    async def fetch_candidates(  # type: ignore[override]
+        self,
+        *,
+        scene: str,
+        user_profile: object,
+        fallback_level: int,
+        limit: int,
+        locale: str,
+        exclude_content_ids: list[int] | None = None,
+    ) -> list[ContentProfileDTO]:
+        # 单测：简化实现，只按 level 返回，忽略 limit/locale/exclude
+        return list(self._candidates_by_level.get(int(fallback_level), []))[: int(limit)]
+
+    async def fetch_contents_by_ids(self, *, content_ids: list[int], locale: str) -> list[ContentProfileDTO]:  # type: ignore[override]
+        raise NotImplementedError
+
+
+def _now() -> datetime:
+    return datetime(2026, 2, 2, 12, 0, 0, tzinfo=timezone.utc)
+
+
+def _user_profile(*, stage: str = "unknown", emotion_score: float | None = 0.5) -> UserProfileV1_2:
+    if stage == "expecting":
+        st = UserStageOneHot(expecting=1, parenting=0, unknown=0)  # type: ignore[arg-type]
+    elif stage == "parenting":
+        st = UserStageOneHot(expecting=0, parenting=1, unknown=0)  # type: ignore[arg-type]
+    else:
+        st = UserStageOneHot(expecting=0, parenting=0, unknown=1)  # type: ignore[arg-type]
+
+    return UserProfileV1_2(
+        profile_generated_at=_now(),
+        profile_confidence=1.0,
+        profile_answered=ProfileAnswered(stage=True, emotion=emotion_score is not None, context=False, need=False),
+        stage=st,
+        emotion_score=emotion_score,
+        context={},
+        need={},
+    )
+
+
+def _content(
+    *,
+    content_id: int,
+    text: str = "hello",
+    stage: str = "general",
+    personalization_power: float = 0.0,
+    risk_flags: list[str] | None = None,
+    author_id: str | None = None,
+    template_id: str | None = None,
+    need_suitability: dict[str, float] | None = None,
+) -> ContentProfileDTO:
+    return ContentProfileDTO(
+        content_id=int(content_id),
+        text=text,
+        stage=stage,  # type: ignore[arg-type]
+        emotion_score=None,
+        context_suitability={},
+        need_suitability=need_suitability or {},
+        personalization_power=float(personalization_power),
+        risk_flags=risk_flags or [],
+        author_id=author_id,
+        template_id=template_id,
+        review_confidence=0.7,
+    )
+
+
+@pytest.mark.asyncio
+async def test_reco_engine_k_zero_returns_empty() -> None:
+    repo = _FakeRepo({0: [_content(content_id=1)]})
+    res = await recommend(
+        repo=repo,
+        scene="feed",
+        user_profile=_user_profile(),
+        already_recommended_ids=[],
+        touched_or_viewed_ids=[],
+        k=0,
+        now=_now(),
+        locale=None,
+    )
+    assert res.items == []
+    assert res.meta.served_k == 0
+
+
+@pytest.mark.asyncio
+async def test_reco_engine_pool_empty_sets_empty_reason() -> None:
+    repo = _FakeRepo({0: []})
+    res = await recommend(
+        repo=repo,
+        scene="feed",
+        user_profile=_user_profile(),
+        already_recommended_ids=[],
+        touched_or_viewed_ids=[],
+        k=1,
+        now=_now(),
+        locale="en",
+    )
+    assert res.items == []
+    assert res.meta.empty_reason == "pool_empty"
+
+
+@pytest.mark.asyncio
+async def test_reco_engine_hard_filter_all_sets_empty_reason() -> None:
+    repo = _FakeRepo({0: [_content(content_id=1, risk_flags=["block_health_medical"])]})
+    res = await recommend(
+        repo=repo,
+        scene="push",
+        user_profile=_user_profile(stage="unknown"),
+        already_recommended_ids=[],
+        touched_or_viewed_ids=[],
+        k=1,
+        now=_now(),
+        locale="en",
+    )
+    assert res.items == []
+    assert res.meta.empty_reason == "hard_filter_all"
+    assert res.meta.risk_filtered_count_by_flag.get("block_health_medical", 0) == 1
+
+
+@pytest.mark.asyncio
+async def test_reco_engine_freqcap_all_sets_empty_reason() -> None:
+    # Push 场景：作者冷却命中，导致 after_freqcap=0
+    repo = _FakeRepo({0: [_content(content_id=1, author_id="a1")]})
+    res = await recommend(
+        repo=repo,
+        scene="push",
+        user_profile=_user_profile(stage="unknown"),
+        already_recommended_ids=[],
+        touched_or_viewed_ids=[],
+        k=1,
+        now=_now(),
+        locale="en",
+        constraints=RecoConstraints(recent_author_ids=["a1"]),
+    )
+    assert res.items == []
+    assert res.meta.empty_reason == "freqcap_all"
+
+
+@pytest.mark.asyncio
+async def test_reco_engine_returns_item_and_explanations_enabled() -> None:
+    repo = _FakeRepo({0: [_content(content_id=1, text="t1", personalization_power=0.0)]})
+    res = await recommend(
+        repo=repo,
+        scene="feed",
+        user_profile=_user_profile(stage="unknown"),
+        already_recommended_ids=[],
+        touched_or_viewed_ids=[],
+        k=1,
+        now=_now(),
+        locale="en",
+    )
+    assert len(res.items) == 1
+    assert res.items[0].content_id == 1
+    assert res.items[0].text == "t1"
+    assert res.items[0].explanations is not None
+
+