A graph recommender model for knowledge graph propagation with collaborative factor
| Autor: | ZHU Xinjuan, TONG Xiaokai, WANG Xihan, GAO Quanli |
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| Jazyk: | čínština |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Xi'an Gongcheng Daxue xuebao, Vol 36, Iss 2, Pp 79-86 (2022) |
| Druh dokumentu: | article |
| ISSN: | 1674-649X 1674-649x |
| DOI: | 10.13338/j.issn.1674-649x.2022.02.011 |
| Popis: | In view of the problems of data sparsity and cold-start in traditional recommender model, the introduction of knowledge graph as side-information can address the above problems and be interpretable. However, knowledge graph is more biased towards propagation of knowledge than user preferences and difficult to capture high-order relations. To solve these problems, collaborative factor module was introduced into propagation-based method in this paper to capture high-order relations and discover latent patterns. In addition, a density gate composed of three co-occurrence matrix density parameters was designed, so that the collaborative factor module could dynamically control the output by the sparsity of the co-occurrence matrix. Contrast experiments were carried out on public film, book and music data sets. The experimental results demonstrate that the model performs well in the click-through-rate scenario, and the indicators are significantly improved on the data sets whose relations of knowledge graph are difficult to explain user preferences. |
| Databáze: | Directory of Open Access Journals |
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