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Paper ID:IJSREM68360

LuminaBook AI: An Intelligent Literary Discovery and Author Empowerment Ecosystem

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM68360

Volume:

10 |

10 |

Oct 2026

Pages:

1-3

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

BVK

B. Varun Kumar

Dept Of Information Technology

Sri Shakthi Institute Of Engineering And Technology

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SNK

S. Navaneeth Kumar

Dept Of Information Technology

Sri Shakthi Institute Of Engineering And Technology

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KSRP

K. S. Rethina Prakash

Dept Of Information Technology

Sri Shakthi Institute Of Engineering And Technology

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RR

R. Rithish

Dept Of Information Technology

Sri Shakthi Institute Of Engineering And Technology

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RS

R. Sabarikirivasan

Dept Of Information Technology

Sri Shakthi Institute Of Engineering And Technology

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Abstract

Digital book discovery is often fragmented across online marketplaces, author websites, publisher platforms, and literary event directories. This paper presents Lumina Book AI, an artificial intelligence-powered literary discovery and reading ecosystem designed to enhance book exploration, purchasing, and author visibility. The proposed platform integrates mood-based book discovery, semantic search, personalized recommendations, and a conversational AI reading assistant to help readers identify books based on their interests and preferences. It also provides forthcoming book recommendations, dedicated author and publisher profiles, external purchasing links, map-based bookstore discovery, and information about upcoming book fairs and literary expos. By connecting readers with books, authors, publishers, and literary events through a unified interface, the system aims to improve content discoverability and encourage engagement with reading culture. The proposed approach emphasizes reliable catalogue information, transparent release details, and responsible AI-assisted recommendations. Lumina Book AI seeks to transform conventional online book browsing into an intelligent, personalized, and community-oriented literary experience while creating greater digital visibility for emerging authors and publishing communities.

KEYWORDS

Artificial intelligence semantic search book recommendation author discovery literary ecosystem book fairs.

Cite This Article

B. Varun Kumar, S. Navaneeth Kumar, K. S. Rethina Prakash, R. Rithish, R. Sabarikirivasan, "LuminaBook AI: An Intelligent Literary Discovery and Author Empowerment Ecosystem," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-3, Oct 2026.

Full Article/Conclusion

 LuminaBook AI is proposed as an intelligent literary discovery and author empowerment ecosystem that brings together mood-based search, semantic discovery, a conversational reading assistant, personalized recommendations, forthcoming-release awareness, book purchasing pathways, author and publisher profiles, map-based shop discovery, and literary event navigation. Its defining ambition is to make books easier to discover while giving authors, publishers, bookshops, and literary gatherings a more connected place in the reader’s journey. By combining responsible AI assistance with structured and verifiable information, the platform can support a richer and more inclusive digital reading culture. Future work should focus on validating the AI features, improving catalogue and event integrations, protecting user data, and assessing the platform with readers and literary stakeholders.

References

1.       Ricci, F., Rokach, L., Shapira, B. (eds.): Recommender Systems Handbook. 3rd edn. Springer, Cham (2022) 2.       Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering 17(6) (2005) 734–749 3.       Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press, Cambridge (2010) 4.          Open Library: Open Library APIs: Book and Author Catalogue Data. Available: https://openlibrary.org/developers/api 5.       Google: Gemini API Documentation. Google AI for Developers. Available: https://ai.google.dev/gemini-api/docs 6.       Google Maps Platform: Developer Documentation. Available: https://developers.google.com/maps/documentation

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Publication Date

10/26/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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