Abstract
In this paper, we proposed a scalable framework W&D (wide & deep framework) plus to capture users’ personal interest for e-commerce recommender systems by combining the advantage of W&D and Residual Units. To better model users’ actual purchase processes, we build an NSPD (N-stage Purchase Decision Model) based on W&D plus by splitting the shopping process into n stages. According to a real scenario, we maximize each stage probability and multiply them as the final probability. Besides, we capture users’ evolving sequential preference to recommend the right product at the right time period. Experimental results on IJCAI 2015 from Tmall and Amazon Clothing, Shoes and Jewelry demonstrate that NSPD can outperform existing state-of-the-art models significantly.
Supported by National Natural Science Foundation of China (grant No. 61402100, 61472075).
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Yan, C., Huang, Y., Zhang, Q., Wan, Y. (2018). NSPD: An N-stage Purchase Decision Model for E-commerce Recommendation. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10987. Springer, Cham. https://doi.org/10.1007/978-3-319-96890-2_13
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