{"title":"数字化促进阅读习惯的养成:改进后的混合图书推荐系统与流派导向型档案","authors":"Onur Dogan, Emre Yalcin, Ouranıa Areta Hiziroglu","doi":"10.1108/lm-03-2024-0030","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>Reading habit plays a pivotal role in individuals' personal and academic growth, making it essential to encourage among campus users. University libraries serve as valuable platforms to promote reading by providing access to a diverse range of books and resources. Recommending books through personalized systems not only helps campus users discover new materials but also enhances their engagement and satisfaction with the library’s offerings, contributing to a holistic learning experience.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>This study presents a web-based solution, the Web-Based Hybrid Intelligent Book Recommender System (W_HybridBook), as a solution that addresses challenges like cold start issues and limited scalability by factoring in user preferences and item similarities in generating book recommendations. The paper improves the traditional hybrid system using Genre-Oriented Profiles (GOPs) instead of original rating profiles of users when determining similarities between individuals. Consumption-based genre profiles (W_HybridBook-CBP) are created by assessing whether an item has received any ratings in the dataset, and vote-based genre profiles (W_HybridBook-VBP) are generated by considering the genre categories based on the magnitude of the user’s rating.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>The comparative results indicated that users are quite satisfied with the recommendations generated by W\\_HybridBook-VBP profiling, with an average rating of 4.0633 and a precision value of 0.7988. W\\_HybridBook-VBP is also the fastest way with respect to the algorithm and recommendation run time.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>The proposed W\\_HybridBook has been then enhanced by adopting two user profiling strategies to boost the similarity calculation process in the recommendation generation phase. This system provides ranking-based recommendations by mainly integrating well-known collaborative and content-based filtering strategies. A dataset has been collected by considering the preferences of both users and academics at Izmir Bakircay University, which is one of the universities with the highest number of books per student. More importantly, this dataset has been released and become publicly available for future research in the recommender system field.</p><!--/ Abstract__block -->","PeriodicalId":46701,"journal":{"name":"Library Management","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Digitalization for enhancing reading habits: the improved hybrid book recommendation system with genre-oriented profiles\",\"authors\":\"Onur Dogan, Emre Yalcin, Ouranıa Areta Hiziroglu\",\"doi\":\"10.1108/lm-03-2024-0030\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3>Purpose</h3>\\n<p>Reading habit plays a pivotal role in individuals' personal and academic growth, making it essential to encourage among campus users. University libraries serve as valuable platforms to promote reading by providing access to a diverse range of books and resources. Recommending books through personalized systems not only helps campus users discover new materials but also enhances their engagement and satisfaction with the library’s offerings, contributing to a holistic learning experience.</p><!--/ Abstract__block -->\\n<h3>Design/methodology/approach</h3>\\n<p>This study presents a web-based solution, the Web-Based Hybrid Intelligent Book Recommender System (W_HybridBook), as a solution that addresses challenges like cold start issues and limited scalability by factoring in user preferences and item similarities in generating book recommendations. The paper improves the traditional hybrid system using Genre-Oriented Profiles (GOPs) instead of original rating profiles of users when determining similarities between individuals. Consumption-based genre profiles (W_HybridBook-CBP) are created by assessing whether an item has received any ratings in the dataset, and vote-based genre profiles (W_HybridBook-VBP) are generated by considering the genre categories based on the magnitude of the user’s rating.</p><!--/ Abstract__block -->\\n<h3>Findings</h3>\\n<p>The comparative results indicated that users are quite satisfied with the recommendations generated by W\\\\_HybridBook-VBP profiling, with an average rating of 4.0633 and a precision value of 0.7988. W\\\\_HybridBook-VBP is also the fastest way with respect to the algorithm and recommendation run time.</p><!--/ Abstract__block -->\\n<h3>Originality/value</h3>\\n<p>The proposed W\\\\_HybridBook has been then enhanced by adopting two user profiling strategies to boost the similarity calculation process in the recommendation generation phase. This system provides ranking-based recommendations by mainly integrating well-known collaborative and content-based filtering strategies. A dataset has been collected by considering the preferences of both users and academics at Izmir Bakircay University, which is one of the universities with the highest number of books per student. More importantly, this dataset has been released and become publicly available for future research in the recommender system field.</p><!--/ Abstract__block -->\",\"PeriodicalId\":46701,\"journal\":{\"name\":\"Library Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2024-08-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Library Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/lm-03-2024-0030\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"INFORMATION SCIENCE & LIBRARY SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Library Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/lm-03-2024-0030","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
Digitalization for enhancing reading habits: the improved hybrid book recommendation system with genre-oriented profiles
Purpose
Reading habit plays a pivotal role in individuals' personal and academic growth, making it essential to encourage among campus users. University libraries serve as valuable platforms to promote reading by providing access to a diverse range of books and resources. Recommending books through personalized systems not only helps campus users discover new materials but also enhances their engagement and satisfaction with the library’s offerings, contributing to a holistic learning experience.
Design/methodology/approach
This study presents a web-based solution, the Web-Based Hybrid Intelligent Book Recommender System (W_HybridBook), as a solution that addresses challenges like cold start issues and limited scalability by factoring in user preferences and item similarities in generating book recommendations. The paper improves the traditional hybrid system using Genre-Oriented Profiles (GOPs) instead of original rating profiles of users when determining similarities between individuals. Consumption-based genre profiles (W_HybridBook-CBP) are created by assessing whether an item has received any ratings in the dataset, and vote-based genre profiles (W_HybridBook-VBP) are generated by considering the genre categories based on the magnitude of the user’s rating.
Findings
The comparative results indicated that users are quite satisfied with the recommendations generated by W\_HybridBook-VBP profiling, with an average rating of 4.0633 and a precision value of 0.7988. W\_HybridBook-VBP is also the fastest way with respect to the algorithm and recommendation run time.
Originality/value
The proposed W\_HybridBook has been then enhanced by adopting two user profiling strategies to boost the similarity calculation process in the recommendation generation phase. This system provides ranking-based recommendations by mainly integrating well-known collaborative and content-based filtering strategies. A dataset has been collected by considering the preferences of both users and academics at Izmir Bakircay University, which is one of the universities with the highest number of books per student. More importantly, this dataset has been released and become publicly available for future research in the recommender system field.
期刊介绍:
■strategic management ■HRM/HRO ■cultural diversity ■information use ■managing change ■quality management ■leadership ■teamwork ■marketing ■outsourcing ■automation ■library finance ■charging ■performance measurement ■data protection and copyright As information services become more complex in nature and more technologically sophisticated, managers need to keep pace with innovations and thinking in the field to offer the most professional service with the resources they have.