Abstract
Personalized recommendation has continuously received attention due to its great commercial value in business. Recently variational auto-encoder is employed in top-N recommendation for its effectiveness in deep collaborative filtering. The key challenge of model-based collaborative filtering is to develop effective latent factors representations with user-item interaction records. In this paper, we present a new class of conditional variational auto-encoders (CVAEs) that utilizes the fact of similar users tending to associate with each other on purchasing preference. This type of conditional variational auto-encoder concentrates on learning with label verification signals to ensure an exclusive latent mean factor for users with the same labels. Moreover, to handle complex multi-label combinations, we extend the model with a split-merge framework by learning labels of different conditional attributes separately and then merge the results from multiple prediction pools. Extensive experiments are conducted on two real-life datasets to simulate both user-based and item-based recommendation scenarios. Experimental results are favorable when comparing with the state-of-art methods.
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Acknowledgements
This paper is supported by the National Key Research and Development Program of China (Grant No. 2016YF- B1001102), the National Natural Science Foundation of China (Grant Nos. 61502227, 61876080), the Collaborative Innovation Center of Novel Software Technology and Industrialization at Nanjing University.
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Pang, B., Yang, M., Wang, C. (2019). A Novel Top-N Recommendation Approach Based on Conditional Variational Auto-Encoder. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11440. Springer, Cham. https://doi.org/10.1007/978-3-030-16145-3_28
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DOI: https://doi.org/10.1007/978-3-030-16145-3_28
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