Manuscript received April 22, 2026; revised May 18, 2026; accepted June 5, 2026; published June 16, 2026
Abstract—This study presents an integrated, data-driven framework for evaluating publishing titles. It leverages big data analytics to improve editorial decision-making. The architecture features: (1) publisher-survey-calibrated indicator weights optimized with the Analytic Hierarchy Process (AHP); (2) automated pipelines that organize bibliographic data into multi-dimensional repositories, categorizing by author, genre, and time; (3) knowledge graphs using Neo4j to synthesize complex relationships among authors, books, and publishers; and (4) standardized assessment benchmarks, including a composite author proficiency metric. This metric is derived from commercial viability, productivity, and reader perception, each scored on a 0–10 scale.
Keywords—book title selection, big data, editorial decision support, Analytic Hierarchy Process (AHP)
[PDF]
Cite: Ma Xiaotian and Wang Chao, "Predictive Analytics for Book Title Selection: A Big Data-Based Study," International Journal of Future Computer and Communication, vol. 15, no. 1, pp. 44-48, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
(CC BY 4.0)
