A Framework for Feature Selection Using Natural Language Processing for User Profile Learning for Recommendations of Healthcare-Related Content

Mona Tanwar, Sunil Kumar Khatri, Ravi Pendse

Source Title: International Journal of Business Analytics (IJBAN)9(3)

ISSN: 2334-4547|EISSN: 2334-4555|EISBN13: 9781683182894|DOI: 10.4018/IJBAN.292059

MLA

Tanwar, Mona, et al. "A Framework for Feature Selection Using Natural Language Processing for User Profile Learning for Recommendations of Healthcare-Related Content." IJBAN vol.9, no.3 2022: pp.1-17. http://doi.org/10.4018/IJBAN.292059

APA

Tanwar, M., Khatri, S. K., & Pendse, R. (2022). A Framework for Feature Selection Using Natural Language Processing for User Profile Learning for Recommendations of Healthcare-Related Content. International Journal of Business Analytics (IJBAN), 9(3), 1-17. http://doi.org/10.4018/IJBAN.292059

Chicago

Tanwar, Mona, Sunil Kumar Khatri, and Ravi Pendse. "A Framework for Feature Selection Using Natural Language Processing for User Profile Learning for Recommendations of Healthcare-Related Content," International Journal of Business Analytics (IJBAN) 9, no.3: 1-17. http://doi.org/10.4018/IJBAN.292059

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

Abstract

This paper presents the work done on recommendations of healthcare related journal papers by understanding the semantics of terms from the papers referred by users in past. In other words, user profiles based on user interest within the healthcare domain are constructed from the kind of journal papers read by the users. Multiple user profiles are constructed for each user based on different categories of papers read by the users. The proposed approach goes to the granular level of extrinsic and intrinsic relationship between terms and clusters highly semantically related relevant domain terms where each cluster represents a user interest area. The semantic analysis of terms is done starting from co-occurrence analysis to extract the intra-couplings between terms and then the inter-couplings are extracted from the intra-couplings and then finally clusters of highly related terms are formed. The experiments showed improved precision for the proposed approach as compared to the state-of-the-art technique with a mean reciprocal rank of 0.76.