Of the People for the People: Digital Literature Resource Knowledge Recommendation Based on User Cognition
We attempt to improve user satisfaction with the effects of retrieval results and visual appearance by employing users’ own information. User feedback on digital platforms has been proven to be one type of user cognition. Through conducting a digital literature resource organization model based on user cognition, our proposal improves both the content and presentation of retrieval systems. This paper takes Powell's City of Books as an example to describe the construction process of a knowledge network. The model consists of two parts. In the unstructured data part, synopses and reviews were recorded as representatives of user cognition. To build the resource category, linguistic and semantic analyses were used to analyze the concepts and the relationships among them. In the structural data part, the metadata of every book was linked with each other by informetrics relationships. The semantic resource was constructed to assist with building the knowledge network. We conducted a mock-up to compare the new category and knowledge-recommendation system with the current retrieval system. Thirty-nine subjects examined our mock-up and highly valued the differences we made for the improvements in retrieval and appearance. Knowledge recommendation based on user cognition was tested to be positive based on user feedback. There could be more research objects for digital resource knowledge recommendations based on user cognition.
Peter Carruthers, Stephen Stich, and Michael Siegal, The Cognitive Basis of Science (Cambridge: Cambridge University Press, 2002).
Sophie Monchaux et al., “Query Strategies during Information Searching: Effects of Prior Domain Knowledge and Complexity of the Information Problems to Be Solved,” Information Processing and Management 51, no. 5 (2015): 557–69, https://doi.org/10.1016/j.ipm.2015.05.004.
Hoill Jung and Kyungyong Chung, “Knowledge-Based Dietary Nutrition Recommendation for Obese Management,” Information Technology and Management 17, no. 1 (2016): 29–42, https://doi.org/10.1007/s10799-015-0218-4.
Dandan Ma, Liren Gan, and Yonghua Cen, “Research on Influence of Individual Cognitive Preferences upon Their Acceptance for Knowledge Classification Recommendation Service,” Journal of the China Society for Scientific and Technical Information 33, no. 7 (2014): 712–29.
Haiqun Ma and Zhihe Yang, “Study on the Cognitive Model of Information Searchers from the Perspective of Neuro-Language Programming,” Journal of Library Science in China 37, no. 3 (2011): 38–47.
Paul Gooding, “Exploring the Information Behaviour of Users of Welsh Newspapers Online through Web Log Analysis,” Journal of Documentation 72, no. 2 (2016): 232–46. https://doi.org/10.1108/JD-10-2014-0149.
Munmun De Choudhury and Scott Counts, “Identifying Relevant Social Media Content : Leveraging Information Diversity and User Cognition,” in ’HT11 Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia (New York: ACM, 2011), 161–70, https://doi.org/10.1145/1995966.1995990; Carol Tenopir et al., “Academic Users’ Interactions with ScienceDirect in Search Tasks: Affective and Cognitive Behaviors,” Information Processing and Management 44, no. 1 (2008): 105–21, https://doi.org/10.1016/j.ipm.2006.10.007.
Young Han Bae, Jong Woo Jun, and Michelle Hough, “Uses and Gratifications of Digital Signage and Relationships with User Interface,” Journal of International Consumer Marketing 28, no. 5 (2016): 323–31, https://doi.org/10.1080/08961530.2016.1189372.
Claude Sicotte et al., “Analysing User Satisfaction with the System in Use Prior to the Implementation of a New Electronic Inpatient Record,” in Proceedings of the 12th World Congress on Health (Medical) Informatics; Building Sustainable Health Systems (Amsterdam: IOS Press, 2007), 1779-1784; Zhenzheng Qian et al., “SatiIndicator: Leveraging User Reviews to Evaluate User Satisfaction of SourceForge Projects,” in Proceedings—International Computer Software and Applications Conference 1 (2016):93–102, https://doi.org/10.1109/COMPSAC.2016.183.
Christina Merten and Cristina Conati, “Eye-Tracking to Model and Adapt to User Meta-Cognition in Intelligent Learning Environments,” in Proceedings of the 11th International Conference on Intelligent User Interfaces—IUI ’06 (New York: ACM, 2006), 39–46, https://doi.org/10.1145/1111449.1111465; Weidong Zhao, Ran Wu, and Haitao Liu, “Paper Recommendation Based on the Knowledge Gap between a Researcher’s Background Knowledge and Research Target,” Information Processing & Management 52, no. 5 (2016): 976–88, https://doi.org/10.1016/j.ipm.2016.04.004.
Haoran Xie et al., “Incorporating Sentiment into Tag-Based User Profiles and Resource Profiles for Personalized Search in Folksonomy,” Information Processing and Management 52, no. 1 (2016): 61–72, https://doi.org/10.1016/j.ipm.2015.03.001; Francisco Villarroel Ordenes et al., “Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach,” Journal of Service Research 17, no. 3 (2014): 278–95, https://doi.org/10.1177/1094670514524625; Yujong Hwang and Jaeseok Jeong, “Electronic Commerce and Online Consumer Behavior Research: A Literature Review,” Information Development 32, no. 3 (2016): 377–88, https://doi.org/10.1177/0266666914551071.
Stephan Ludwig et al., “More Than Words: The Influence of Affective Content and Linguistic Style Matches in Online Reviews on Conversion Rates,” Journal of Marketing 77, no. 1 (2012): 1–52, https://doi.org/10.1509/jm.11.0560.
Jun Yang and Yinglong Wang, “A New Framework Based on Cognitive Psychology for Knowledge Discovery,” Journal of Software 8, no. 1 (2013): 47–54.
Alan Baddeley, “On Applying Cognitive Psychology,” British Journal of Psychology 104, no. 4 (2013): 443–56, https://doi.org/10.1111/bjop.12049.
Aidan Moran, “Cognitive Psychology in Sport: Progress and Prospects,” Psychology of Sport and Exercise 10, no. 4 (2009): 420–26, https://doi.org/10.1016/j.psychsport.2009.02.010.
John Van De Pas, “A Framework for Public Information Services in the Twenty-First Century,” New Library World 114, no. 1/2 (2013): 67–79, https://doi.org/10.1108/03074801311291974.
Enrique Frias-Martinez, Sherry Y. Chen, and Xiaohui Liu, “Evaluation of a Personalized Digital Library Based on Cognitive Styles: Adaptivity vs. Adaptability,” International Journal of Information Management 29, no. 1 (2009): 48–56, https://doi.org/10.1016/j.ijinfomgt.2008.01.012.
Shing Lee Chung et al., “An Integrated Framework for Managing Knowledge-Intensive Service Innovation,” International Journal of Services Technology and Management 13, no. 1/2 (2010): 20, https://doi.org/10.1504/IJSTM.2010.029669.
Koteshwar Chirumalla, “Managing Knowledge for Product-Service System Innovation: The Role of Web 2.0 Technologies,” Research-Technology Management 56, no. 2 (2013): 45–53, https://doi.org/10.5437/08956308X5602045; Koteshwar Chirumalla et al., “Knowledge-Sharing Network for Product-Service System Development: Is It a Typical?,” in International Conference on Industrial Product-Service Systems (2013): 109–14; Fumiya Akasaka et al., “Development of a Knowledge-Based Design Support System for Product-Service Systems,” Computers in Industry 63, no. 4 (2012): 309–18, https://doi.org/10.1016/j.compind.2012.02.009.
C. F. Cheung et al., “A Multi-Perspective Knowledge-Based System for Customer Service Management,” Expert Systems with Applications 24, no. 4 (2003): 457–70, https://doi.org/10.1016/S0957-4174(02)00193-8.
Padmal Vitharana, Hemant Jain, and Fatemeh Zahedi, “A Knowledge Based Component/Service Repository to Enhance Analysts’ Domain Knowledge for Requirements Analysis,” Information and Management 49, no. 1 (2012): 24–35, https://doi.org/10.1016/j.im.2011.12.004.
Baihai Zhou, “The Construction of Library Interdisciplinary Knowledge Sharing Service System,” in 2014 11th International Conference on Service Systems and Service Management (ICSSSM), June 25–27, 2014, https://doi.org/10.1109/ICSSSM.2014.6874033.
Rusli Abdullah, Zeti Darleena Eri, and Amir Mohamed Talib, “A Model of Knowledge Management System for Facilitating Knowledge as a Service (KaaS) in Cloud Computing Environment,” 2011 International Conference on Research and Innovation in Information Systems, November 23–24, 2011, 1–4, https://doi.org/10.1109/ICRIIS.2011.6125691.
Alan Smeaton and Jamie Callan, “Personalisation and Recommender Systems in Digital Libraries,” International Journal on Digital Libraries 5, no. 4 (2005): 299–308, https://doi.org/10.1007/s00799-004-0100-1.
Yanwen Wu et al., “Research on Personalized Knowledge Service System in Community E-Learning,” Lecture Notes in Computer Science (Berlin: Springer, 2006), https://doi.org/10.1007/11736639_17; Shu-Chen Kao and ChienHsing Wu, “PIKIPDL. A Personalized Information and Knowledge Integration Platform for DL Service,” Library Hi Tech 30, no. 3 (2012): 490–512, https://doi.org/10.1108/07378831211266627.
National Geographic, Destinations of a Lifetime: 225 of the World’s Most Amazing Places (Washington D.C.: National Geographic Society, 2016).
Wen Lou and Junping Qiu, “Semantic Information Retrieval Research Based on Co-Occurrence Analysis,” Online Information Review 38, no. 1 (January 8, 2014): 4–23, https://doi.org/10.1108/OIR-11-2012-0203; Junping Qiu and Wen Lou, “Constructing an Information Science Resource Ontology Based on the Chinese Social Science Citation Index,” Aslib Journal of Information Management 66, no. 2 (March 10, 2014): 202–18, https://doi.org/10.1108/AJIM-10-2013-0114; Fan Yu, Junping Qiu, and Wen Lou, “Library Resources Semantization Based on Resource Ontology,” Electronic Library 32, no. 3 (2014): 322–40, https://doi.org/10.1108/EL-05-2012-0056.
Lei Zhang et al., “Extracting and Ranking Product Features in Opinion Documents,” in International Conference on Computational Linguistics (2010): 1462–70.
Lou and Qiu, “Semantic Information Retrieval Research,” 4; Qiu and Lou, “Constructing an Information Science Resource Ontology,” 202; Yu, Qiu, and Lou, “Library Resources Semantization,” 322.
Qiu and Lou, “Constructing an Information Science Resource Ontology,” 202.