Abstract
Because a wide range of professionals utilize Social Network Service (SNS), the SNS users have recently required an expert recommendation service to enable users to perform both cooperation and technical communication with experts. A content-boosted collaborative filtering (CBCF) provides various prediction algorithms which support effective recommendations. However, the CBCF cannot calculates the similarity of items (or users) when the calculation condition is not clearly provided. To solve the problem, we propose a content-aware hybrid collaborative filtering scheme for expert recommendation in SNSs. Finally, we show from a performance analysis that our scheme outperforms the existing method in terms of recommendation accuracy.
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Acknowledgments
This work was supported by the Human Resource Training Program for Regional Innovation and Creativity through the Ministry of Education and National Research Foundation of Korea (NRF-2014H1C1A1065816) and also supported by the Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. R0113-15-0005, Development of an Unified Data Engineering Technology for Large-scale Transaction Processing and Real-time Complex Analytics).
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Shin, YS., Kim, HI., Chang, JW. (2016). A Content-Aware Expert Recommendation Scheme in Social Network Services. In: Park, J., Jin, H., Jeong, YS., Khan, M. (eds) Advanced Multimedia and Ubiquitous Engineering. Lecture Notes in Electrical Engineering, vol 393. Springer, Singapore. https://doi.org/10.1007/978-981-10-1536-6_7
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DOI: https://doi.org/10.1007/978-981-10-1536-6_7
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