中文    English

›› 2018, Vol. 30 ›› Issue (2): 74-79.doi: 10.13998/j.cnki.issn1002-1248.2018.02.010

• Network technology • Previous Articles     Next Articles

Reading Resource Recommendation Model Based on Mobile Scene Fusion

LIN Shuzhen   

  1. Guangzhou Library, Guangdong Guangzhou 510623, China
  • Received:2017-10-19 Revised:2018-03-01 Online:2018-02-05 Published:2018-03-01

Abstract: In mobile networks, reading requests and interests have high birth-death frequency and complex data structures, which are difficultly monitored and utilized by the server of library. In order to deal with them, a reading resource recommendation model was proposed based on mobile scene fusion. And its frameworks, working flows and key sub-algorithms were given. It used the mobile scenes as reading atlas nodes, the relations of mobile scenes as weighted reading atlas edges, and the grey fuzzy to integrate the library collection and reading requires, and customized the reading resource recommendation catalog. The experimental results showed that the model had higher client request coverage, better recommendation matching degrees and cost performance.

Key words: library service

CLC Number: 

  • G250.7
[1] Bela Gipp, J·ran Beel, Christian Hentschel. Scienstein: A Research Paper Recommender System[J]. Journal of Libraries,2017,23(1):39-45.
[2] Michael Hahsler. Recommenderlab: A Framework for Developing and Testing Recommendation Algorithms[J]. Journal of Digital Libraries, 2016,3(8):55-94.
[3] Ankit Khera. Online Library Recommendation System[J]. Journal of Computers, 2016,11(9):375-426.
[4] Swapneel Sheth, Nipun Arora, Christian Murph. weHelp: A Reference Architecture for Social Recommender Systems [J]. Journal of User Interface Software, 2017,9(4):23-49.
[5] Daniel Mican, Loredana Mocean, Nicolae Tomai. Building a Social Recommender System by Harvesting Social Relationships and Trust Scores Between Users [J]. Journal of Libraries, 2016,10(5):62-101.
[6] Yifan Hu, Yehuda Koren, Chris Volinsky. Collaborative Filtering for Implicit Feedback Datasets for Libraries [J]. Journal of Digital Information Processing, 2015,13(2):235-276.
[7] Yu Wang. User Data Analytics and Recommender System for Discovery Engine [D]. Sweden:Royal Institute of Technology, Stockholm, 2015.
[8] Joeran Beel, Bela Gipp, Akiko Aizawa. DLib: Recommendations-as-a-Service (RaaS) for Academia [J]. Journal of Information System,2015,1(12):190-216.
[9] John Ben Schafer, Joseph A Konstan, John Riedl. Meta-recommendation Systems: User-controlled Integration of Diverse Recommendations [J]. Journal of Information Service, 2015,10(8):323-351.
[10] Karen Smith-Yoshimura, Rose Holley. Recommendations and Readings: Social Metadata for Libraries, Archives, and Museums [J]. Journal of Network Information System, 2016,11(4):66-97.
[11] Raymond J Mooney, Loriene Roy. Content-Based Book Recommending Using Learning for Text Categorization [J]. International Journal of Electronic Libraries,2014,3(12):195-210.
[1] WANG Sufang, TAN Qingan. How to Create a New Digital Culture Environment for Teenagers in Poor Rural Areas Under the Background of Rural Revitalization: Based on a Study on Internet Use Behavior and Library Services Needs of Left-Behind Children [J]. Journal of Library and Information Science in Agriculture, 2022, 34(1): 16-37.
[2] CAO Qi. System Analysis of the Next-generation Library Service Platform Based on Microservice Architecture——Taking FOLIO as an Example [J]. Journal of Library and Information Science in Agriculture, 2020, 32(4): 51-58.
[3] ZHENG Yufei, WANG Zheng. Characteristics and Trends of Overseas Library Open Access Activities During the Epidemic Period [J]. Journal of Library and Information Science in Agriculture, 2020, 32(12): 20-28.
[4] DING Jingda, LU Ying. Digital Humanities Education Practice of Stanford University and Library Support Services [J]. Agricultural Library and Information, 2019, 31(11): 15-22.
[5] WU Piaosheng. Comparative Study on Mobile Reading Service between Jiangxi's Universities and Other Domestic Famous Universities [J]. , 2018, 30(8): 138-142.
[6] ZENG Yao. Factors Affecting the Acceptance of Mobile Service APP Users in University Library: A Study Based on Technology Adoption Model [J]. , 2018, 30(6): 53-56.
[7] WANG Rongrong, WU Haiying. Research on the Deep Integration Path of Artificial Intelligence and Library Service [J]. , 2018, 30(11): 141-144.
[8] LI Jingcheng, WENG Changping. Discussion on the Path of Innovation and Entrepreneurship Education Service for University Library [J]. , 2018, 30(1): 155-158.
[9] ZUO Hong. Construction of Digital Library’s Cloud Service [J]. , 2017, 29(8): 164-168.
[10] LI Yan. The Change of Service Behavior of Public Libraries from the View of Sustainable Development [J]. , 2017, 29(6): 171-173.
[11] YAN Chong. Design and Implementation of API Interface for Library’s Wechat public platform Based on PHP [J]. , 2017, 29(6): 51-54.
[12] XIE Meimei. Research on the Impact of Mobile Reading on Reader Behavior and Library Service [J]. , 2017, 29(4): 176-179.
[13] ZHAO Benping, GAO Changpeng. Study on Space Embedded Service in the Transformation of University Library Based on the Perspective of Oral Reading Users [J]. , 2017, 29(4): 172-175.
[14] LI Yue. Design and Implementation of Website Building Program for Member Libraries of Shaanxi Public Library Service Consortia [J]. , 2017, 29(3): 38-42.
[15] LIU Li, QI Xiuxia. Study on Reference Service of University Library in MOOC Environment [J]. , 2017, 29(2): 168-170.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!