Abstract
Since collaborative filtering algorithm cannot make full use of video attribute information, mining implicit information of video, this paper proposes a video recommendation algorithm based on knowledge reasoning of knowledge graph. The algorithm uses a knowledge graph with powerful semantic processing capabilities and open interconnection capabilities. The ontology model is used to formalize the implicit semantics in the data. By using the knowledge inference method of the knowledge graph, the existing triplet information is used to establish new relationships between entities and assign corresponding weights to the paths. All the semantic information is embedded in the low-dimensional vector space, and the potential semantic similarity of the video is calculated by combining the path weights. And then, integrate semantic similarity into collaborative filtering for recommendation. Experiments show that this algorithm can make up for the deficiency that collaborative filtering algorithms cannot fully utilize the hidden information of video, and enhance the effectiveness of recommendation at the semantic level, what is more, the recommendation results are interpretable. To a certain extent, it can solve the problem of data sparseness.
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Acknowledgements
This work of the paper is supported by National Natural Science Foundation of China (No.61702157), Science and Technology Support Program of Hebei Province of China (No.15210506), and Natural Science Foundation of Tianjin (No.16JCQNJC00400, No. 16JCYBJC15600).
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Xu, Z., Zhao, X., Dong, Y., Yan, W., Yu, Z. (2018). Research on Video Recommendation Algorithm Based on Knowledge Reasoning of Knowledge Graph. In: U, L., Xie, H. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 11268. Springer, Cham. https://doi.org/10.1007/978-3-030-01298-4_14
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DOI: https://doi.org/10.1007/978-3-030-01298-4_14
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