Abstract
Predicting the intention of users for different commodities has been receiving more and more attention in many applications, such as the decision of awarding bonus and the recommendation of commodity in E-commerce. Existing methods treat customer-to-commodity data as a flat data sequence while ignoring intrinsic multi-modality nature. Observing that different modalities (e.g., click ratios, collection, and purchase amount), as well as the elements within each modality, contribute differently to the prediction of purchasing intention. Besides, existing methods cannot handle the sparsity problem well. As a result, they cannot predict the user intention with sparse data. To address these issues, in this paper, we present a novel Dynamical User Intention Prediction via Multi-modal Learning (DUIPML) method to integrate different types of data to dynamically predict the user intention while reducing the impact of sparse data, which can be well applied on the practical bonus awarding scenario. Specifically, we firstly design a multi-modal fusion strategy to integrate different types of behavior information to obtain the initial user intention of each customer for each category. Next, we treat different clients with different preferences as two modalities and proposed a multi-modal alignment strategy to explore the latent correlations between different clients. After that, we communicate knowledge between the two clients based on the correlations to complete and enrich the user intention for each other, and thus to alleviate the issue of data sparsity. We apply the enriched user intention for the practical bonus awarding scenario on the Taobao platform in Alibaba group. Experiments on benchmark multi-modal datasets and the realistic E-commodity scenarios show that our method significantly outperforms related representative approaches both on effectiveness and adaptability.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akoglu, L., Tong, H., Koutra, D.: Graph based anomaly detection and description: a survey. Data Min. Knowl. Dis. 29(3), 626–688 (2014). https://doi.org/10.1007/s10618-014-0365-y
Baltruaitis, T., Ahuja, C., Morency, L.P.: Multimodal machine learning: a survey and taxonomy. TPAMI 41(2), 423–443 (2019)
Bronstein, M.M., Bronstein, A.M., Michel, F., Paragios, N.: Data fusion through cross-modality metric learning using similarity-sensitive hashing. In: CVPR, pp. 3594–3601 (2010)
Graves, A., Mohamed, A.r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: ICASSP, pp. 6645–6649 (2013)
Guo, Q., et al.: Securing the deep fraud detector in large-scale e-commerce platform via adversarial machine learning approach. In: The World Wide Web Conference, pp. 616–626 (2019)
Guo, Y., Ding, G., Han, J., Gao, Y.: Sitnet: discrete similarity transfer network for zero-shot hashing. In: IJCAI, pp. 1767–1773 (2017)
Hooi, B., et al.: Fraudar: bounding graph fraud in the face of camouflage. In: KDD, pp. 895–904 (2016)
Hotelling, H.: Relations between two sets of variates. Biometrika 28(3/4), 321–377 (1936)
Hu, Z., Zhang, J., Li, Z.: General robustness evaluation of incentive mechanism against bounded rationality using continuum-armed bandits. AAAI 33, 6070–6078 (2019)
Jiang, Q.Y., Li, W.J.: Deep cross-modal hashing. In: CVPR, pp. 3270–3278 (2017)
Kumar, S., Udupa, R.: Learning hash functions for cross-view similarity search. In: IJCAI, pp. 1360–1365 (2011)
Lei, C., Ji, S., Li, Z.: Tissa: a time slice self-attention approach for modeling sequential user behaviors. In: The World Wide Web Conference, pp. 2964–2970 (2019)
Li, Z., et al.: Fair: fraud aware impression regulation system in large-scale real-time e-commerce search platform. In: ICDE, pp. 1898–1903 (2019)
Lin, Z., Ding, G., Han, J., Wang, J.: Cross-view retrieval via probability-based semantics-preserving hashing. IEEE Trans. on Cybern. 47(12), 4342–4355 (2017)
Lin, Z., Ding, G., Hu, M., Wang, J.: Semantics-preserving hashing for cross-view retrieval. In: CVPR, pp. 3864–3872 (2015)
Liu, X., Yu, G., Domeniconi, C., Wang, J., Ren, Y., Guo, M.: Ranking-based deep cross-modal hashing. In: AAAI, pp. 4400–4407 (2019)
Liu, X., Yu, G., Domeniconi, C., Wang, J., Xiao, G., Guo, M.: Weakly-supervised cross-modal hashing. IEEE Trans. on Big Data 6(99), 1–12 (2019)
Long, M., Yue, C., Wang, J., Yu, P.S.: Composite correlation quantization for efficient multimodal retrieval. In: SIGIR, pp. 579–588 (2016)
Ranjan, V., Rasiwasia, N., Jawahar, C.V.: Multi-label cross-modal retrieval. In: ICCV, pp. 4094–4102 (2015)
Shao, W., He, L., Lu, C.T., Wei, X., Philip, S.Y.: Online unsupervised multi-view feature selection. In: ICDM, pp. 1203–1208 (2016)
Shen, F., Zhou, X., Yu, J., Yang, Y., Liu, L., Shen, H.T.: Scalable zero-shot learning via binary visual-semantic embeddings. TIP 99(1), 1–10 (2019)
Shi, X., Yu, P.: Dimensionality reduction on heterogeneous feature space. In: ICDM, pp. 635–644 (2012)
Thompson, D.J.: A theory of sampling finite universes with arbitrary probabilities (1952)
Wang, J., Kumar, S., Chang, S.F.: Semi-supervised hashing for large-scale search. TPAMI 34(12), 2393–2406 (2012)
Wang, J., Zhang, T., Sebe, N., Shen, H.T.: A survey on learning to hash. TPAMI 40(4), 769–790 (2018)
Wang, J., Liu, W., Kumar, S., Chang, S.F.: Learning to hash for indexing big data - a survey. Proc. IEEE 104(1), 34–57 (2016)
Wang, K., He, R., Wang, W., Wang, L., Tan, T.: Learning coupled feature spaces for cross-modal matching. In: ICCV, pp. 2088–2095 (2013)
Weng, H., et al.: Cats: cross-platform e-commerce fraud detection. In: ICDE, pp. 1874–1885 (2019)
Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemom. Intell. Lab. Syst. 2(1–3), 37–52 (1987)
Xu, Y., Yang, Y., Shen, F., Xu, X., Zhou, Y., Shen, H.T.: Attribute hashing for zero-shot image retrieval. In: ICME, pp. 133–138 (2017)
Yang, Y., Luo, Y., Chen, W., Shen, F., Jie, S., Shen, H.T.: Zero-shot hashing via transferring supervised knowledge. In: ACM MM, pp. 1286–1295 (2016)
Yu, J., Wang, M., Tao, D.: Semisupervised multiview distance metric learning for cartoon synthesis. TIP 21(11), 4636–4648 (2012)
Zhang, D., Li, W.J.: Large-scale supervised multimodal hashing with semantic correlation maximization. In: AAAI, pp. 2177–2183 (2014)
Zhang, Y., Ahmed, A., Josifovski, V., Smola, A.: Taxonomy discovery for personalized recommendation. In: WSDM, pp. 243–252 (2014)
Zhou, J., Ding, G., Guo, Y.: Latent semantic sparse hashing for cross-modal similarity search. In: ACM SIGIR, pp. 415–424 (2014)
Ziegler, C.N., Lausen, G., Schmidt-Thieme, L.: Taxonomy-driven computation of product recommendations. In: CIKM, pp. 406–415 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, X. et al. (2020). Dynamical User Intention Prediction via Multi-modal Learning. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12112. Springer, Cham. https://doi.org/10.1007/978-3-030-59410-7_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-59410-7_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59409-1
Online ISBN: 978-3-030-59410-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)