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Deep Heterogeneous Autoencoders for Collaborative Filtering
- Source :
- ICDM
- Publication Year :
- 2018
- Publisher :
- arXiv, 2018.
-
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.<br />Comment: Proceedings of the IEEE International Conference on Data Mining, pp. 1164-1169, Singapore, 2018
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
Deep Autoencoder
Feature vector
Feature extraction
02 engineering and technology
Recommender system
Machine learning
computer.software_genre
MovieLens
Data modeling
Computer Science - Information Retrieval
Machine Learning (cs.LG)
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Collaborative filtering
Categorical variable
business.industry
Heterogeneous Data
Autoencoder
Computer science and engineering [Engineering]
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Information Retrieval (cs.IR)
Subjects
Details
- ISSN :
- 11641169
- Database :
- OpenAIRE
- Journal :
- ICDM
- Accession number :
- edsair.doi.dedup.....ce1bc84b011610996c1c22a5ae754b6f
- Full Text :
- https://doi.org/10.48550/arxiv.1812.06610