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                <text>Conference Papers</text>
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              <text>PMFRO: Personalized Mens Fashion Recommendation Using Dynamic Ontological Models</text>
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              <text>Fashion Recommendation; Mens Fashion Recommendation; Ontology; Semantically Driven; User Click Records</text>
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              <text>There is a thriving need for an expert intelligent system for recommending fashion especially focusing on mens fashion. As it is an area which is neglected both in terms of fashion and modelling intelligent systems. So, in this paper the PMFRO framework for mens recommendation has been put forth which indicates the semantic similarity schemes with auxiliary knowledge and machine intelligence in a very systematic manner. The framework intelligently creates mapping of the preprocessed preferences and the user records and clicks with that of the items in the profile. So, this model aggregates community user profiles and also maps the mens fashion ontology using strategic semantic similarity schemes. Semantic similarity is evaluated using Lesk similarity and NPMI measures at several stages and instances with differential set thresholds and the dataset is classified using the feature control, machine learning bagging classifier which is an ensemble model in order to recommend the mens fashion. The PMFRO framework is an intelligent amalgamation and integration of auxiliary knowledge, strategic knowledge, user profile preferences as well as machine learning paradigms and semantic similarity models for recommending mens fashion and overall precision of 94.68% and FDR of 0.06 was achieved using the PMFRO model.  2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.</text>
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              <text>Arunkumar S.; Deepak G.; Priyadarshini J.S.; Santhanavijayan A.</text>
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              <text>Lecture Notes in Networks and Systems, Vol-647 LNNS, pp. 96-105.</text>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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              <text>2023-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-3-031-27409-1_9" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-3-031-27409-1_9&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163277359&amp;amp;doi=10.1007%2F978-3-031-27409-1_9&amp;amp;partnerID=40&amp;amp;md5=243aefafd38db7c10dca94c4c08548b8" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163277359&amp;amp;doi=10.1007%2f978-3-031-27409-1_9&amp;amp;partnerID=40&amp;amp;md5=243aefafd38db7c10dca94c4c08548b8&lt;/a&gt;</text>
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              <text>ISSN: 23673370; ISBN: 978-303127408-4</text>
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              <text>Online</text>
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              <text>Arunkumar S., Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India; Deepak G., Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India; Priyadarshini J.S., Deparment of Data Science, CHRIST (Deemed to Be University), Bengaluru, India; Santhanavijayan A., National Institute of Technology, Tiruchirappalli, India</text>
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