AI Book Recommendation Engine（AI 书籍推荐引擎）

Expert book recommendation engine via web search. Finds high-quality books (Douban ≥7.5 or Goodreads ≥3.8) based on topic, with deduplication and comprehensive scoring. Use when you need to recommend books for reading tasks, skill building, or research.

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 Type	Template	Example
Chinese 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 Field	Action
rating missing after 2 attempts	Use LLM estimate from search context (mark as "rating_source": "estimated"). If no context at all, drop the book.
review_count missing	Default to 500 (neutral — neither penalized nor boosted)
publish_date missing	Default to 2020
author_nationality missing	Output "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