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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 9/2020

07.01.2019 | Cognitive Computing for Intelligent Application and Service

Distributed representation learning via node2vec for implicit feedback recommendation

verfasst von: Yezheng Liu, Zhiqiang Tian, Jianshan Sun, Yuanchun Jiang, Xue Zhang

Erschienen in: Neural Computing and Applications | Ausgabe 9/2020

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Abstract

As an important technology of Internet products, the recommender system can help users to obtain the information they need and alleviate the problem of information overload. In the implicit feedback recommender system, the key issue is how to represent users and products. In recent years, deep learning has achieved good performance in many fields including speech recognition, computer vision and natural language processing. We propose a deep learning-enhanced framework for implicit feedback recommendation. In this framework, we simultaneously learn the new distributed representation of users and items via node2vec to improve the negative sampling strategy. Finally, we develop a deep neural network recommendation model to integrate user features, product features and interaction features. Experiments conducted on two real-world datasets demonstrate the effectiveness of the proposed framework and methods.
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Fußnoten
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Metadaten
Titel
Distributed representation learning via node2vec for implicit feedback recommendation
verfasst von
Yezheng Liu
Zhiqiang Tian
Jianshan Sun
Yuanchun Jiang
Xue Zhang
Publikationsdatum
07.01.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 9/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-03964-2

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