Book Scout#

Expert book recommendation engine that finds high-quality books via web search.

When to Use#

• Recommending books for a specific topic (e.g., "user growth", "decision science")
• Finding books for reading tasks (morning/noon/evening reading reports)
• Building a reading list for skill development
• Need to avoid previously analyzed books

Input#

• topic (required): Subject/theme (e.g., "用户增长", "决策科学", "AI技术")
• used_models (optional): Array of book title strings to exclude (e.g., ["《精益创业》", "《从0到1》"])

Output#

JSON object with the highest-scoring book:

json

{
  "book_title": "书名",
  "author": "作者",
  "author_nationality": "国籍或'未知'",
  "publish_date": "YYYY-MM或YYYY",
  "rating": 8.9,
  "review_count": 15000,
  "score": 112.08,
  "summary": "100字核心简介",
  "reasoning": "推荐理由"
}

Core Workflow (Two-Phase Search)#

Phase 1: Discover Book Titles#

Goal: Get a list of 5-8 candidate book names. Do NOT try to get ratings here.

Search Queries (execute 2-3 queries in parallel):

Query TypeTemplateExampleChinese book lists"{topic} 经典书籍推荐 书单"``"用户增长 经典书籍推荐 书单"English book lists"{topic_en} best books goodreads"``"user growth best books goodreads"Community picks"{topic} 必读书 知乎推荐"``"用户增长 必读书 知乎推荐"

Extract: Collect book titles + authors from search results. Ignore ratings at this stage.

Deduplicate immediately: Compare against used_models — remove any matches.

Minimum: Need at least 3 candidate books after dedup. If fewer, broaden the topic and search again.

Phase 2: Get Ratings (Per-Book Lookup)#

Goal: Get accurate rating + review_count for each candidate.

Strategy (try in order, stop at first success):

Method A: WebFetch Douban Page (Preferred)

For each candidate book, search for its Douban page then fetch it:

1. web_search: "{book_title}" site:book.douban.com
2. If a book.douban.com/subject/ URL is found → web_fetch that URL
3. Extract: rating, review_count, publish_date, author from the page

Why this works: Douban book pages have structured rating data that WebFetch can reliably parse.

Method B: Direct Search (Fallback)

If Method A fails (no Douban URL found, or WebFetch blocked):

• web_search: "{book_title}" "{author}" 豆瓣评分 评价人数
• Extract rating and review_count from search snippets

Method C: Goodreads Lookup (For English Books)

• web_search: "{book_title}" "{author}" site:goodreads.com
• If URL found → web_fetch the Goodreads page
• Extract rating and ratings_count

Important Rules:

• Each book gets its OWN individual lookup — never combine multiple books into one query
• Each book gets up to 2 attempts (e.g., Method A fails → try Method B)
• Process books in parallel when possible

Phase 2.5: Handle Missing Data#

After Phase 2, some books may still lack ratings. Apply these rules:

Missing FieldActionrating missing after 2 attemptsUse LLM estimate from search context (mark as "rating_source": "estimated"). If no context at all, drop the book.review_count missingDefault to 500 (neutral — neither penalized nor boosted)publish_date missingDefault to 2020author_nationality missingOutput "未知" (NEVER fabricate)

LLM Estimation Rule: If multiple search results consistently describe a book as "高分" / "经典" / "highly rated" but no exact number is found, estimate conservatively (7.5-8.0 for Chinese, 3.8-4.0 for English). Always mark estimated ratings.

Phase 3: 3D Scoring Algorithm#

Action: Collect ALL surviving candidate books into a single JSON array. Pass this entire array to scripts/score_books.py via stdin for batch scoring. The script returns sorted results.

(If script unavailable, calculate manually using the formula below.)

Formula:

text

Total Score = (Base Quality + Popularity Bonus) × Recency Multiplier

A. Base Quality:

text

Base = rating × 10
If review_count < 100: Base = Base × 0.8 (small sample penalty)

B. Popularity Bonus:

text

Bonus = log₁₀(review_count) × 2

C. Recency Multiplier (based on publish_date):

text

Published within 2 years (2024-now):  × 1.2
Published 3-5 years ago (2021-2023):  × 1.0
Published 5+ years ago (≤2020):       × 0.8

Example:

text

《增长黑客》: rating=8.5, review_count=10000, publish=2015
Base = 8.5 × 10 = 85
Bonus = log₁₀(10000) × 2 = 8
Recency = 0.8
Total = (85 + 8) × 0.8 = 74.4

Phase 4: Output#

Return the highest-scoring book in the structured JSON format.

Reasoning field must include: score justification, recency consideration, author background (if known).

If rating_source is "estimated", add a note: "注意：评分为根据多源信息估算，非精确数据"

Quality Filters#

Minimum Standards:

• Douban rating ≥ 7.5 OR Goodreads rating ≥ 3.8
• Estimated ratings: apply the same thresholds

Exclusions:

• Books with "21天", "速成", "一本通" in title
• Marketing-heavy books with no substance

Fallback & Error Handling#

Scenario 1: Web Search Failure#

• Retry once after 2-3 seconds
• If still fails, try alternative query phrasing
• After 3 total failures, return error:

json

{
  "error": "网络连接连续 3 次超时，无法获取最新书单数据，请稍后重试。"
}

Scenario 2: Topic Too Niche#

• Broaden search: remove professional jargon, use parent category
• Example: "认知负荷理论" → "认知心理学 经典书籍"

If broad search also fails:

json

{
  "error": "该主题下未找到具备足够评价数据的经典书籍，请尝试更换更宽泛的主题或行业大词。"
}

Scenario 3: All Candidates Dropped#

If after Phase 2.5 no books survive:

• Return to Phase 1 with broader topic
• Lower quality filter temporarily to ≥ 7.0 / ≥ 3.5
• If still nothing, return the best estimated candidate with a warning

Implementation Notes#

• Phase 1 (discover): pure web_search, focus on book list articles
• Phase 2 (ratings): web_search + web_fetch combo, target Douban/Goodreads pages
• Phase 3 (scoring): scripts/score_books.py (deterministic)
• Parallelism: Phase 1 queries can run in parallel; Phase 2 per-book lookups can run in parallel
• Prioritize Douban/Goodreads/Zhihu/Reddit sources; ignore ads and promotional content