Of the People for the People: Digital Literature Resource Knowledge Recommendation Based on User Cognition

Wen Lou, Hui Wang, Jiangen He

Abstract


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.


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DOI: https://doi.org/10.6017/ital.v37i3.10060

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