Neural Network-based Collaborative Filtering Algorithm

A Neural Network-based Collaborative Filtering Algorithm is an deep learning-based recommendation algorithm that is a collaborative filtering algorithm.

• Context:
  • It can be implemented by a Deep Neural Network-based Collaborative Filtering System.
• Example(s):
  • Neural Autoregressive Distribution Estimator for Collaborative Filtering (CF-NADE) (Zheng et al., 2016).
  • …
• Counter-Example(s):
  • Matrix Factorization-based Collaborative Filtering Algorithm.
• See: Domain Specific Recommendation Task, Algorithm-Specific Task.

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References

2019

• (Zhang et al., 2019) ⇒ Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. (2019). “Deep Learning based Recommender System: A Survey and New Perspectives.” ACM Computing Surveys (CSUR) 52, no. 1
  • QUOTE: ... More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. ...

    ... ...

2017

• (He et al., 2017) ⇒ Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. (2017). “Neural Collaborative Filtering.” In: Proceedings of the 26th International Conference on world wide web (WWW-2017).
  • QUOTE: ... By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. ...

2016a

• (Wu et al., 2016) ⇒ Yao Wu, Christopher DuBois, Alice X. Zheng, and Martin Ester. (2016). “Collaborative Denoising Auto-Encoders for Top-N Recommender Systems.” In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. ISBN:978-1-4503-3716-8 doi:10.1145/2835776.2835837
  • QUOTE: Most real-world recommender services measure their performance based on the top-N results shown to the end users. Thus, advances in top-N recommendation have far-ranging consequences in practical applications. In this paper, we present a novel method, called Collaborative Denoising Auto-Encoder (CDAE), for top-N recommendation that utilizes the idea of Denoising Auto-Encoders.

2016b

• (Zheng et al., 2016) ⇒ Yin Zheng, Bangsheng Tang, Wenkui Ding, and Hanning Zhou. (2016). “A Neural Autoregressive Approach to Collaborative Filtering.” In: Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48.

2015

• (Wang et al., 2015) ⇒ Hao Wang, Naiyan Wang, and Dit-Yan Yeung. (2015). “Collaborative Deep Learning for Recommender Systems.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783273
  • QUOTE: Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recently advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix.

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