Abstract
Research on the hot topics and future development trends of recommender system is of great significance for improving the accuracy of recommendation results and saving users’ time. A total of 867 SCI and SSCI literatures related to recommender system were selected from the Web of Science database from 2013 to July 2022. The visual knowledge graph analysis tool CiteSpace was used to analyze the temporal and spatial distribution characteristics, knowledge basis, research hotspots and frontiers of personalized recommendation technology research from five dimensions: literature growth trend, regional distribution, literature co-citation relationship, keyword co-occurrence and emergence. The participation of Chinese and American researchers are much higher than that of other countries and the exchange and cooperation between countries need to be strengthened. The research focuses on the application of algorithm in recommender system, improvement of collaborative filtering algorithm, model construction, social network and neural network. The research frontiers include feature extraction of users and projects, machine learning and attention mechanism.
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Wan, J., Lin, S., Zhang, J. (2023). Bibliometric Analysis on the Research Hotspots of Recommender Systems. In: Tu, Y., Chi, M. (eds) E-Business. Digital Empowerment for an Intelligent Future. WHICEB 2023. Lecture Notes in Business Information Processing, vol 480. Springer, Cham. https://doi.org/10.1007/978-3-031-32299-0_1
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