Academic Paper Search

Academic Paper Search

Search academic databases, retrieve abstracts and metadata, rank by relevance, and download PDFs for literature review integration.

Workflow

1. Build Context Profile

Extract keywords from user's project to guide search and relevance scoring.

Sources to check:

• LaTeX files (project/**/*.tex) for technical terms, methods, research gaps
• Existing bibliography (references.bib) for cited authors/papers
• Markdown notes (references-new/, *.md)
• Uploaded PDFs in papers/
• User-provided keywords

Extract: technical terms, domain keywords, acronyms, research questions, evaluation metrics

Example context:

Domain: recommender systems, collaborative filtering
Methods: deep learning, neural networks
Gap: interpretability
Time: recent (2020-2025)

2. Search Academic Sources

Use fetch_webpage to search multiple databases in parallel:

Primary sources:

• Google Scholar: scholar.google.com/scholar?q={query}&as_ylo={year} - broad coverage, citation counts
• arXiv: arxiv.org/search/?query={query}&order=-submitted_date - full abstracts, open access PDFs
• Semantic Scholar: semanticscholar.org/search?q={query} - citation metrics, influence scores
• IEEE Xplore: ieeexplore.ieee.org/search/searchresult.jsp?queryText={query} - engineering/CS focus

Fetch pattern:

fetch_webpage(
  urls=["https://scholar.google.com/...", "https://arxiv.org/...", ...],
  query="Extract title, authors, year, abstract, venue, citations, DOI/PDF links for {keywords}"
)

Include time filters (e.g., &as_ylo=2020) for recent work requests.

3. Extract & Structure Results

Format each paper:

### [Paper Title](DOI_URL)
**Authors**: First Author, et al.  
**Venue**: Conference/Journal, Year  
**Citations**: [count]  
**Relevance**: [score/100] ⭐⭐⭐⭐⭐

**Abstract**: [full text or snippet + "Partial abstract"]

**PDF**: [link] or "Institutional access required"

4. Rank by Relevance (0-100 Scale)

Scoring dimensions:

1. Keyword Match (30pts): Primary in title (+15 each), primary in abstract (+5), secondary (+2)
2. Conceptual Alignment (25pts): Same research gap (+15), methodology (+10), domain (+10)
3. Recency (15pts): 2024-25 (+15), 2022-23 (+12), 2020-21 (+8), older decreases
4. Citation Impact (15pts): >100 cites (+15), 50-100 (+12), 20-49 (+8), 10-19 (+5)
5. Venue Quality (10pts): A* venue (+10), A (+8), B (+5), workshop (+3)
6. Context Fit (5pts): Cites user's papers (+5), author overlap (+3), same dataset (+4)

Sort descending; show top 10 with score breakdown for highly relevant papers (>80).

5. Download PDFs (On Request)

When user asks "download PDFs" or "extract papers to papers/":

Create tools/download_papers.py:

import requests, os
from pathlib import Path

def download_pdf(url, filename, dest="papers"):
    Path(dest).mkdir(exist_ok=True)
    try:
        r = requests.get(url, headers={'User-Agent': 'Mozilla/5.0'}, timeout=30)
        if r.status_code == 200 and 'pdf' in r.headers.get('Content-Type', ''):
            with open(f"{dest}/{filename}", 'wb') as f: f.write(r.content)
            return True, f"{dest}/{filename}"
        return False, f"Status {r.status_code}"
    except Exception as e: return False, str(e)

# Add paper URLs and run
papers = [("https://arxiv.org/pdf/2301.12345.pdf", "smith2023.pdf")]
for url, name in papers:
    ok, msg = download_pdf(url, name)
    print(f"{'✓' if ok else '✗'} {name}: {msg}")

Run via run_in_terminal. Handle errors: 403/401 = institutional access, 404 = broken link.

After downloads, suggest: python tools/extract_papers.py and python tools/review_papers.py.

Output Example

## Literature Search: "Explainable Recommender Systems"

**Context**: project/capitulo2 — collaborative filtering, deep learning, interpretability gap  
**Query**: explainable recommender systems deep learning 2020-2025  
**Sources**: Google Scholar, arXiv, Semantic Scholar, IEEE

---

### 🥇 [Attention-Based Explanations for Collaborative Filtering](https://doi.org/10.1145/3383313.3412345)
**Relevance: 92/100** ⭐⭐⭐⭐⭐  
**Authors**: Silva, Smith, Costa  
**Venue**: RecSys 2023  
**Citations**: 47  
**PDF**: [arXiv](https://arxiv.org/pdf/2301.12345.pdf)

**Abstract**: Attention-based neural CF model with post-hoc explanations using multi-head attention. NDCG@10 +4.2% over baseline, 78% explanation quality. Addresses interpretability gap in deep learning recommenders...

**Score breakdown**: Keyword 28/30, Alignment 25/25, Recency 15/15, Citations 9/15, Venue 10/10, Fit 5/5

---

[... 4-9 more papers ...]

---

**Summary**: 23 found (showing top 10), avg 68/100, 5 highly relevant (>80), 3 public PDFs

**Next steps**:
1. Download PDFs?
2. Add to references.bib?
3. Draft Chapter 3 table?

Integration

• Bibliography: Invoke bibtex-reference-management to add entries
• PDF extraction: Run tools/extract_papers.py and tools/review_papers.py after downloads
• TCC Chapter 3: Generate comparative table with \ticV/\ticX markers

Common Requests

• "Find papers on [topic]": Extract context, search 3-4 sources, return top 10
• "Recent work": Filter last 2-3 years, prioritize arXiv/preprints
• "Papers from [venue]": Add venue-specific search, use IEEE Xplore/ACM DL
• "Download top 5": Create script, filter public PDFs, report success/failures
• "Papers like [uploaded_pdf]": Extract with extract_papers.py, boost alignment weight

Error Handling

• No results: Suggest broader keywords, alternative terms, extended time range
• Source unavailable: Continue with other sources, note which failed
• Paywalled PDFs: Provide DOI, suggest institutional access or preprints
• Limited context: Request research focus/methods/domain or proceed with keywords only

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