Abstract
Out-of-town recommendation aims to provide Point-of-Interest (POI) recommendation when users leave their hometown and arrive in a new city. To infer the out-of-town preferences of cold-start users based on their hometown preferences, some recent methods directly train a mapping function between users’ hometown preferences and out-of-town preferences. Unfortunately, they depend on a large number of overlapping users who left check-in histories in both the home city and the out-of-town city to build the mapping relationships. Also, they don’t fully explore the category hierarchy knowledge of POIs, which can help with robust POI representations. To this end, in this paper, we propose Adversarial Cycle-Consistent Autoencoder for Category-Aware Out-of-Town Recommendation named ACCAC, which effectively learns the mapping function even in the case that the number of overlapping users is limited. Specifically, we first utilize denoising autoencoders to learn pre-trained POI embeddings augmented with category hierarchy knowledge. Then we introduce a cycle-consistent generative adversarial network to explore potential mapping relationships. Extensive experiments on real-world out-of-town recommendation datasets demonstrate the effectiveness of ACCAC.
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Qin, L., Lian, D. (2022). Adversarial Cycle-Consistent Autoencoder for Category-Aware Out-of-Town Recommendation. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13370. Springer, Cham. https://doi.org/10.1007/978-3-031-10989-8_40
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DOI: https://doi.org/10.1007/978-3-031-10989-8_40
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