EXPLORING THE POTENTIAL OF A GENERATIVE AI FOR BOOKS RECOMMENDATION SYSTEM FOR UNIVERSITY STUDENTS
DOI:
https://doi.org/10.65164/5f7wp960Kalit so‘zlar:
book recommendation system; database management system; generative AI; collaborative filtering; entity-relationship model; SQL; MySQL; matrix factorization; university library; personalized recommendation.Abstrak
This research paper presents the design of a Books Recommendation Database Management System (Books Recommendation DBMS) for university libraries and student portals. Most existing recommenders suggest only one book, which is not enough in an academic setting where one topic may be covered better across chapters of several textbooks. To close this gap, we combine a relational database, an Extended Entity-Relationship model, an SQL implementation, and a Generative AI layer that returns multi-book recommendations tuned to the reader's major, year of study, GPA and topic interests. The paper covers data specification and logical requirements, conceptual and logical design, the physical schema built in MySQL Workbench, functional requirements, system architecture, and a small set of SQL experiments that validate the database. Findings on collaborative filtering, k-Nearest Neighbors, Singular Value Decomposition and Stochastic Gradient Descent shaped the recommendation logic and the cold-start strategy. The result is a modular base that can be extended later with more AI and NLP components.
Havolalar
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