Abstract
Heterogeneous graph (HG) representation is closely related with the real-world applications, as heterogeneous objects and interactions are ubiquitous in many practical systems. HG representation methods deployed in real-world system should consider capturing the complex interactions among objects as well as solving the unique challenges existing in real-world systems, such as large-scale, dynamics, and multi-source information. In this chapter, we focus on summarizing the industrial-level applications with HG representation. Particularly, we introduce several well deployed systems that have demonstrated the success of HG representation techniques in resolving real-world applications, including cash-out user detection, intent recommendation, share recommendation, and friend-enhanced recommendation. For industrial-level applications, we pay more attention on two key components: HG construction with industrial data and graph representation techniques on the HG.
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Notes
- 1.
In this chapter, the original attributes are discretized to the same dimension.
- 2.
The original attributes are discretized to sparse D-dimensional feature as the model input.
- 3.
Terms are important words or phrases. We use the AliWS (Alibaba Word Segmenter) to segment the queries and items’ titles and select important words or phrases that contain rich meanings.
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The number of visitors who performed a click.
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- 6.
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Shi, C., Wang, X., Yu, P.S. (2022). Heterogeneous Graph Representation for Industry Application. In: Heterogeneous Graph Representation Learning and Applications. Artificial Intelligence: Foundations, Theory, and Algorithms. Springer, Singapore. https://doi.org/10.1007/978-981-16-6166-2_9
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