Dashboard de Referencias Bibliograficas
Gestion automatizada via pyzotero · Biblioteca: CULTIVA IA Research · Ultima sincronizacion: 16 jun 2026, 09:42
zot = Zotero(library_id='4821073', library_type='group', api_key=os.environ['ZOTERO_API_KEY'])
42
Items Totales
4
Colecciones
18
Etiquetas Unicas
11
Con Adjuntos PDF
8
Anadidos este mes
📁 Colecciones
4 colecciones
IA & Marketing
key: A3F9X2M
Automatizacion de Contenido
key: B7K2P4N
Analisis de Datos
key: C1L8Q7R
Agentes Autonomos
key: D5M3T9S
🏆 Distribucion por Tipo
5 tipos
Journal Article
18
Preprint
12
Book Section
6
Web Page
4
Report
2
Codigo Python
# Obtener distribucion de tipos
items = zot.everything(zot.items())
tipos = {}
for item in items:
t = item['data']['itemType']
tipos[t] = tipos.get(t, 0) + 1
🏷 Nube de Etiquetas
zot.tags()
📄 Referencias Recientes
Mostrando 10 de 42
zot.top(limit=10)
| Titulo / Autores | Tipo | Anyo | Coleccion | Etiquetas |
|---|---|---|---|---|
|
Generative AI for Personalized Marketing at Scale
Journal of Marketing Research, Vol. 61(2)
|
Article | 2024 | IA & Marketing |
LLM
marketing-digital
content-generation
|
|
Retrieval-Augmented Generation: A Survey
arXiv:2312.10997
|
Preprint | 2023 | Agentes Autonomos |
RAG
vector-search
embeddings
|
|
Autonomous Agents for Business Process Automation
Harvard Business Review, Digital Edition
|
Article | 2025 | Agentes Autonomos |
agents
automation
multi-agent
|
|
The State of AI in Content Marketing 2025
HubSpot State of Marketing Report
|
Report | 2025 | IA & Marketing |
marketing-digital
SEO
automation
|
|
Fine-Tuning Large Language Models for Domain-Specific Tasks
NeurIPS 2024 Workshop on LLMs
|
Preprint | 2024 | Automatizacion |
fine-tuning
transformers
NLP
|
|
Natural Language Processing for Marketing Analytics
Cambridge University Press, 3rd ed.
|
Book | 2023 | Analisis de Datos |
NLP
data-analysis
machine-learning
|
|
Prompt Engineering for Marketers: Practical Techniques
Medium · Towards Data Science
|
Web | 2025 | IA & Marketing |
prompt-engineering
LLM
|
|
Vector Databases for Semantic Search in E-Commerce
SIGIR 2024 Conference Proceedings
|
Article | 2024 | Analisis de Datos |
vector-search
embeddings
RAG
|
|
AI-Driven CRO: Optimizing Conversion with Machine Learning
Conversion XL Research Blog
|
Web | 2025 | IA & Marketing |
conversion-rate
machine-learning
automation
|
|
Evaluation of LLM-Generated Marketing Copy Quality
Journal of Advertising Research, Vol. 64(1)
|
Article | 2025 | Automatizacion |
evaluation
content-generation
LLM
|
🔗 Exportacion BibTeX (muestra)
format='bibtex'
@article{Chen2024Generative,
author = {Chen, Lin and Patel, Rohan and Nguyen, Anh},
title = {{Generative AI for Personalized Marketing at Scale}},
journal = {Journal of Marketing Research},
year = {2024},
volume = {61},
number = {2},
pages = {148--173},
doi = {10.1177/00222437241234567},
}
@misc{Gao2023RAG,
author = {Gao, Yunfan and Xiong, Yun and others},
title = {{Retrieval-Augmented Generation: A Survey}},
year = {2023},
eprint = {2312.10997},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
}
@techreport{HubSpot2025State,
author = {{HubSpot Research}},
title = {{The State of AI in Content Marketing 2025}},
institution = {HubSpot Inc.},
year = {2025},
url = {https://hubspot.com/state-of-marketing/2025},
}
Snippet Python para exportar:
# Exportar coleccion completa a BibTeX
zot.add_parameters(format='bibtex')
bib_db = zot.everything(
zot.collection_items('A3F9X2M')
)
import bibtexparser
with open('cultiva_ia_marketing.bib', 'w') as f:
bibtexparser.dump(bib_db, f)
⚡ CRUD rapido
Crear Item
tpl = zot.item_template(
'journalArticle')
tpl['title'] = 'Nuevo Paper'
tpl['creators'][0] = {
'creatorType': 'author',
'firstName': 'Ana',
'lastName': 'Lopez'
}
zot.create_items([tpl])
Buscar
results = zot.items(
q='RAG marketing',
limit=20
)
Tags masivos
items = zot.items(tag='LLM')
for it in items:
it['data']['tags'].append(
{'tag':'genai'})
zot.update_item(it)