Abstract
Demographic attributes are crucial for characterizing different types of users in developing market strategy. However, in retail scenario, individual demographic information is not often available due to the difficult manual collection process. Several studies focus on inferring users’ demographic attribute based on their transaction histories, but there is a common problem. Hardly work has introduced knowledge for purchase data embedding. Specifically, purchase data is informative, full of related knowledge entities and common sense. However, existing methods are unaware of such external knowledge and latent knowledge-level connections among items. To address the above problem, we propose a Knowledge-Aware Embedding (KAE) method that incorporates knowledge graph representation into demographic prediction. The KAE is a multi-channel and item-entity-aligned knowledge-aware convolutional neural network that fuses frequency-level and knowledge-level representations of purchase data. Through extensive experiments on a real world dataset, we demonstrate that KAE achieves substantial gains on state-of-the-art demographic prediction models.
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Jiang, Y., Tang, W., Gao, N., Xiang, J., Su, Y. (2019). Demographic Prediction from Purchase Data Based on Knowledge-Aware Embedding. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-030-36802-9_32
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DOI: https://doi.org/10.1007/978-3-030-36802-9_32
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