Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
- Authors
- Qingyao AiXu ChenYongfeng Zhang
- Journal
- Algorithms
In The Last Decade
doi.org/10.3390/a11090137 →Countries where authors are citing Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
This map shows the geographic impact of Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation more than expected).
Fields of papers citing Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
This network shows the impact of Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation.
About Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
This paper, published in 2018, received 283 indexed citations . Written by Qingyao Ai, Xu Chen and Yongfeng Zhang covering the research area of Artificial Intelligence and Information Systems. It is primarily cited by scholars working on Artificial Intelligence (251 citations), Information Systems (232 citations) and Computer Vision and Pattern Recognition (29 citations). Published in Algorithms.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.3390/a11090137.