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Title: Deep heterogeneous autoencoders for Collaborative Filtering
Authors: Li, Tianyu
Ma, Yukun
Xu, Jiu
Stenger, Björn
Liu, Chen
Hirate, Yu
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Li, T., Ma, Y., Xu, J., Stenger, B., Liu, C., & Hirate, Y. (2018). Deep heterogeneous autoencoders for Collaborative Filtering. Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM), 1164-1169. doi:10.1109/icdm.2018.00153
Abstract: This paper leverages heterogeneous auxiliary information to address the data sparsity problem of recommender systems. We propose a model that learns a shared feature space from heterogeneous data, such as item descriptions, product tags and online purchase history, to obtain better predictions. Our model consists of autoencoders, not only for numerical and categorical data, but also for sequential data, which enables capturing user tastes, item characteristics and the recent dynamics of user preference. We learn the autoencoder architecture for each data source independently in order to better model their statistical properties. Our evaluation on two MovieLens datasets and an e-commerce dataset shows that mean average precision and recall improve over state-of-the-art methods.
ISBN: 978-1-5386-9160-1
DOI: 10.1109/ICDM.2018.00153
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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