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A Video Recommendation Algorithm Based on Hyperlink-Graph Model

A Video Recommendation Algorithm Based on Hyperlink-Graph Model

Songtao Shang, Wenqian Shang, Minyong Shi, Shuchao Feng, Zhiguo Hong
Copyright: © 2017 |Volume: 5 |Issue: 3 |Pages: 15
ISSN: 2166-7160|EISSN: 2166-7179|EISBN13: 9781522515555|DOI: 10.4018/IJSI.2017070104
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MLA

Shang, Songtao, et al. "A Video Recommendation Algorithm Based on Hyperlink-Graph Model." IJSI vol.5, no.3 2017: pp.49-63. http://doi.org/10.4018/IJSI.2017070104

APA

Shang, S., Shang, W., Shi, M., Feng, S., & Hong, Z. (2017). A Video Recommendation Algorithm Based on Hyperlink-Graph Model. International Journal of Software Innovation (IJSI), 5(3), 49-63. http://doi.org/10.4018/IJSI.2017070104

Chicago

Shang, Songtao, et al. "A Video Recommendation Algorithm Based on Hyperlink-Graph Model," International Journal of Software Innovation (IJSI) 5, no.3: 49-63. http://doi.org/10.4018/IJSI.2017070104

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Abstract

The traditional graph-based personal recommendation algorithms mainly depend the user-item model to construct a bipartite graph. However, the traditional algorithms have low efficiency, because the matrix of the algorithms is sparse and it cost lots of time to compute the similarity between users or items. Therefore, this paper proposes an improved video recommendation algorithm based on hyperlink-graph model. This method cannot only improve the accuracy of the recommendation algorithms, but also reduce the running time. Furthermore, the Internet users may have different interests, for example, a user interest in watching news videos, and at the same time he or she also enjoy watching economic and sports videos. This paper proposes a complement algorithm based on hyperlink-graph for video recommendations. This algorithm improves the accuracy of video recommendations by cross clustering in user layers.

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