CAIT: A Predictive Tool for Supporting the Book Market Operation Using Social Networks
Source abstract
Abstract
A new predictive support tool for the publishing industry is presented in this note. It consists of a combined model of Artificial Intelligence techniques (CAIT) that seeks the most optimal prediction of the number of book copies, finding out which is the best segmentation of the book market, using data from the networks social and the web. Predicted sales appear to be more accurate, applying machine learning techniques such as clustering (in this specific case, KMeans) rather than using current publishing industry expert’s segmentation. This identification has important implications for the publishing sector since the forecast will adjust more to the behavior of the stakeholders than to the skills or knowledge acquired by the experts, which is a certain way that may not be sufficient and/or variable throughout the period.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Text Analysis Techniques · Digital Marketing and Social Media
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Research Group of Data Science for the Digital Society, La Salle-Ramon Llull University, 08024 Barcelona, Spain
References (34)
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