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Big Data Mining and Analytics  2018, Vol. 01 Issue (03): 211-221    DOI: 10.26599/BDMA.2018.9020019
    
A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering
Yu Liu, Shuai Wang, M. Shahrukh Khan, Jieyu He*
Yu Liu, Shuai Wang, M. Shahrukh Khan, and Jieyue He are with School of Computer Science and Engineering, and also with MOE Key Laboratory of Computer Network and Information Integration, Southeast University, Nanjing 211189, China. E-mail: 220153311@seu.edu.cn; 53263887@ qq.com; mushahrukhkhan@outlook.com.
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Abstract  

Due to the widespread availability of implicit feedback (e.g., clicks and purchases), some researchers have endeavored to design recommender systems based on implicit feedback. However, unlike explicit feedback, implicit feedback cannot directly reflect user preferences. Therefore, although more challenging, it is also more practical to use implicit feedback for recommender systems. Traditional collaborative filtering methods such as matrix factorization, which regards user preferences as a linear combination of user and item latent vectors, have limited learning capacities and suffer from data sparsity and the cold-start problem. To tackle these problems, some authors have considered the integration of a deep neural network to learn user and item features with traditional collaborative filtering. However, there is as yet no research combining collaborative filtering and content-based recommendation with deep learning. In this paper, we propose a novel deep hybrid recommender system framework based on auto-encoders (DHA-RS) by integrating user and item side information to construct a hybrid recommender system and enhance performance. DHA-RS combines stacked denoising auto-encoders with neural collaborative filtering, which corresponds to the process of learning user and item features from auxiliary information to predict user preferences. Experiments performed on the real-world dataset reveal that DHA-RS performs better than state-of-the-art methods.



Key wordshybrid recommender system      neural collaborative filtering      auto-encoder      implicit feedback     
Received: 31 January 2018      Published: 13 January 2020
Corresponding Authors: Jieyu He   
Cite this article:

Yu Liu, Shuai Wang, M. Shahrukh Khan, Jieyu He. A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering. Big Data Mining and Analytics, 2018, 01(03): 211-221.

URL:

http://bigdata.tsinghuajournals.com/10.26599/BDMA.2018.9020019     OR     http://bigdata.tsinghuajournals.com/Y2018/V01/I03/211

NotationDescription
UUser set
IItem set
pDimension of user features
qDimension of item features
LNumber of layers of SDAE
𝑹|𝑈|×|𝐼|Rating matrix
𝑿|𝑈|×𝑝Auxiliary information of users
𝒀|𝐼|×𝑞Auxiliary information of items
𝒑uUser latent vector
𝒒iItem latent vector
𝑾lWeight matrix of layer-l in SDAE
𝒃lBias vector of layer-l in SDAE
Table 1 Frequently used symbols.
Fig. 1 Deep hybrid recommender system based on auto-encoders model.
Fig. 2 A four-layer stacked denoising auto-encoder.
DatasetNumber of usersNumber of itemsSparsity (%)
MovieLens-1M6040370695.8
Table 2 MovieLens-1M statistics.
DatasetUser attributeItem attribute
MovieLens-1MAge, occupation, genderGenre
Table 3 User and item attributes.
Fig. 3 Performances of HR@10 and NDCG@10 w.r.t. the dimension of latent vector.
Fig. 4 Performances of HR@<i>K</i> and NDCG@<i>K</i> w.r.t. <i>K</i>.
Fig. 5 HR@10 and NDCG@10 w.r.t the number of iterations.
Fig. 6 Training loss w.r.t. number of iterations.
Fig. 7 Trade-off effect on HR@10 and NDCG@10.
Fig. 8 Performances of HR@10 and NDCG@10 w.r.t. the number of hidden layers.
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