Abstract
Promotions are becoming more important and prevalent in e-commerce to attract customers and boost sales, leading to frequent changes of occasions, which drives users to behave differently. In such situations, most existing Click-Through Rate (CTR) models can’t generalize well to online serving due to distribution uncertainty of the upcoming occasion. In this paper, we propose a novel CTR model named MOEF for recommendations under frequent changes of occasions. Firstly, we design a time series that consists of occasion signals generated from the online business scenario. Since occasion signals are more discriminative in the frequency domain, we apply Fourier Transformation to sliding time windows upon the time series, obtaining a sequence of frequency spectrums which is then processed by Occasion Evolution Layer (OEL). In this way, a high-order occasion representation can be learned to handle the online distribution uncertainty. Moreover, we adopt multiple experts to learn feature representations from multiple aspects, which are guided by the occasion representation via an attention mechanism. Accordingly, a mixture of feature representations is obtained adaptively for different occasions to predict the final CTR. Experimental results on real-world datasets validate the superiority of MOEF and online A/B tests also show MOEF outperforms representative CTR models significantly.
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Notes
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To the extent of our knowledge, there are no public datasets suited for this promotion-aware CTR prediction task.
References
Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: Deepfm: a factorization-machine based neural network for ctr prediction. In: IJCAI (2017)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Comput. 3(1), 79–87 (1991)
Li, X., Wang, C., Tong, B., Tan, J., Zeng, X., Zhuang, T.: Deep time-aware item evolution network for click-through rate prediction. In: CIKM, pp. 785–794 (2020)
Liu, B., et al.: Autofis: automatic feature interaction selection in factorization models for click-through rate prediction. In: KDD, pp. 2636–2645 (2020)
Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(11) (2008)
Niu, X., Li, B., Li, C., Tan, J., Xiao, R., Deng, H.: Heterogeneous graph augmented multi-scenario sharing recommendation with tree-guided expert networks. In: WSDM, pp. 1038–1046 (2021)
Pan, X., et al.: Metacvr: conversion rate prediction via meta learning in small-scale recommendation scenarios. In: SIGIR, pp. 2110–2114 (2022)
Pi, Q., Bian, W., Zhou, G., Zhu, X., Gai, K.: Practice on long sequential user behavior modeling for click-through rate prediction. In: KDD, pp. 2671–2679 (2019)
Ren, K., et al.: Lifelong sequential modeling with personalized memorization for user response prediction. In: SIGIR, pp. 565–574 (2019)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285–295 (2001)
Shen, Q., Tao, W., Zhang, J., Wen, H., Chen, Z., Lu, Q.: Sar-net: a scenario-aware ranking network for personalized fair recommendation in hundreds of travel scenarios. In: CIKM, pp. 4094–4103 (2021)
Sheng, X.R., et al.: One model to serve all: star topology adaptive recommender for multi-domain ctr prediction. In: CIKM, pp. 4104–4113 (2021)
Soliman, S.S., Srinath, M.D.: Continuous and discrete signals and systems. Englewood Cliffs (1990)
Vaswani, A., et al.: Attention is all you need. In: NeurIPS, pp. 5998–6008 (2017)
Wang, J., Louca, R., Hu, D., Cellier, C., Caverlee, J., Hong, L.: Time to shop for valentine’s day: shopping occasions and sequential recommendation in e-commerce. In: WSDM, pp. 645–653 (2020)
Xiao, Z., Yang, L., Jiang, W., Wei, Y., Hu, Y., Wang, H.: Deep multi-interest network for click-through rate prediction. In: CIKM, pp. 2265–2268 (2020)
Yue, Z., et al.: Ts2vec: towards universal representation of time series. In: AAAI (2022)
Zhang, J., Tao, D.: Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things. IEEE Internet Things J. 8(10), 7789–7817 (2020)
Zhou, G., et al.: Deep interest evolution network for click-through rate prediction. In: AAAI (2019)
Zhou, G., et al.: Deep interest network for click-through rate prediction. In: KDD (2018)
Zhu, C., et al.: Aim: automatic interaction machine for click-through rate prediction. IEEE Trans. Knowl. Data Eng. (2021)
Zhuang, H., Wang, X., Bendersky, M., Najork, M.: Feature transformation for neural ranking models. In: SIGIR, pp. 1649–1652 (2020)
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Pan, X. et al. (2023). MOEF: Modeling Occasion Evolution in Frequency Domain for Promotion-Aware Click-Through Rate Prediction. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13944. Springer, Cham. https://doi.org/10.1007/978-3-031-30672-3_22
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DOI: https://doi.org/10.1007/978-3-031-30672-3_22
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