Semantic Term-Term Coupling-Based Feature Enhancement of User Profiles in Recommendation Systems

Mona Tanwar, Sunil Kumar Khatri, Ravi Pendse

Source Title: Journal of Cases on Information Technology (JCIT)24(3)

ISSN: 1548-7717|EISSN: 1548-7725|EISBN13: 9781799878223|DOI: 10.4018/JCIT.20220701.oa1

MLA

Tanwar, Mona, et al. "Semantic Term-Term Coupling-Based Feature Enhancement of User Profiles in Recommendation Systems." JCIT vol.24, no.3 2022: pp.1-16. http://doi.org/10.4018/JCIT.20220701.oa1

APA

Tanwar, M., Khatri, S. K., & Pendse, R. (2022). Semantic Term-Term Coupling-Based Feature Enhancement of User Profiles in Recommendation Systems. Journal of Cases on Information Technology (JCIT), 24(3), 1-16. http://doi.org/10.4018/JCIT.20220701.oa1

Chicago

Tanwar, Mona, Sunil Kumar Khatri, and Ravi Pendse. "Semantic Term-Term Coupling-Based Feature Enhancement of User Profiles in Recommendation Systems," Journal of Cases on Information Technology (JCIT) 24, no.3: 1-16. http://doi.org/10.4018/JCIT.20220701.oa1

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Abstract

Content-based recommender system is a subclass of information systems that recommends an item to the user based on its description. It suggests items such as news, documents, articles, webpages, journals, and more to users as per their inclination by comparing the key features of the items with key terms or features of user interest profiles. This paper proposes the new methodology using Non-IIDness based semantic term-term coupling from the content referred by users to enhance recommendation results. In the proposed methodology, the semantic relationship is analyzed by estimating the explicit and implicit relationship between terms. It associates terms that are semantically related in real world or are used inter-changeably such as synonyms. The underestimated features of user profiles have been enhanced after term-term relation analysis which results in improved similarity estimation of relevant items with the user profiles.The experimentation result proves that the proposed methodology improves the overall search and retrieval results as compared to the state-of-art algorithms.