Attention based collaborative filtering

Fu, Mingsheng; Qu, Hong; Moges, Dagmawi; Lu, Li

doi:10.1016/j.neucom.2018.05.049

Cited by 38 publications

(18 citation statements)

References 14 publications

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“…Third, the user's latent feature matrix P and the item's latent feature matrix Q are connected as the input of the deep neural network, and then multi-level nonlinear interaction is performed, and finally the prediction score is output. The calculation process of the multi-layer network structure is shown in formula (19). ( 17) in which P u denotes the latent vector of user u, q i denotes the latent vector of item i, R ui is the real score, is the regularization parameter, and W are the activation functions and weights of the hidden layer, respectively.…”

Section: Parameter Learningmentioning

confidence: 99%

“…2. PHD model [19] PHD is a hybrid model, which uses aSDAE and CNN models to extract user and item features, respectively. 3.…”

Section: Baselinesmentioning

confidence: 99%

“…With further deep learning research, an important breakthrough is to introduce the attention mechanism into deep learning, thereby emphasizing local key information. Yin et al [18,19] used the attention mechanism in the recommendation system to learn the user's recent interests from the user's short-term interaction records. Guo et al [20] used the attention machine to make feature extraction for users and items.…”

Section: Introductionmentioning

confidence: 99%

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Attention neural collaboration filtering based on GRU for recommender systems

Xia

Luo

Liu

2021

Complex Intell. Syst.

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The collaborative filtering method is widely used in the traditional recommendation system. The collaborative filtering method based on matrix factorization treats the user’s preference for the item as a linear combination of the user and the item latent vectors, and cannot learn a deeper feature representation. In addition, the cold start and data sparsity remain major problems for collaborative filtering. To tackle these problems, some scholars have proposed to use deep neural network to extract text information, but did not consider the impact of long-distance dependent information and key information on their models. In this paper, we propose a neural collaborative filtering recommender method that integrates user and item auxiliary information. This method fully integrates user-item rating information, user assistance information and item text assistance information for feature extraction. First, Stacked Denoising Auto Encoder is used to extract user features, and Gated Recurrent Unit with auxiliary information is used to extract items’ latent vectors, respectively. The attention mechanism is used to learn key information when extracting text features. Second, the latent vectors learned by deep learning techniques are used in multi-layer nonlinear networks to learn more abstract and deeper feature representations to predict user preferences. According to the verification results on the MovieLens data set, the proposed model outperforms other traditional approaches and deep learning models making it state of the art.

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Section: Parameter Learningmentioning

confidence: 99%

“…2. PHD model [19] PHD is a hybrid model, which uses aSDAE and CNN models to extract user and item features, respectively. 3.…”

Section: Baselinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Attention neural collaboration filtering based on GRU for recommender systems

Xia

Luo

Liu

2021

Complex Intell. Syst.

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“…Compared with manually extracted features, the advantage of rating data is that it is straightforward to get from users, which does not require prior knowledge. Various kinds of collaborative filtering approaches ( [10], [11], [13], [18], [19], [21], [30] etc.) have been developed to improve the quality of the recommendations based on rating data.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Deep Learning Model to Infer User Demographic Information From Ratings

Liu

Chen

et al. 2019

IEEE Access

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Obtaining demographics of online users is of great significance to Internet service providers and advertisers. Most previous works used standard machine learning methods to infer user demographics from handcrafted features. This has two disadvantages. First, the handcrafted features are usually not robust and rely too much on expert experience. Second, these low-capacity models can neither model the complex nonlinear relationship between users nor recognize interdependencies among items. To address these problems, we propose a DEep REtentive learning frameworK (DEREK) for demographic information prediction. Specifically, we introduce a heuristic data generation method that can alleviate data sparsity in order to use rating data more efficiently than the handcrafted features. Moreover, the retention blocks based on high-capacity deep neural networks are designed to extract a share representation from input rating data. Finally, the DEREK can simultaneously infer different demographic attributes through end-to-end multilabel learning architecture. The extensive experimental results on Movielens 100-k and Movielens 1-M data sets have demonstrated the superiority of the proposed DEREK compared with standard machine learning methods (logistic regression and SVM). INDEX TERMS User demographics, rating data, feature extractor, multi-task multi-class prediction, deep learning.

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“…It is helpful in discovering different users' interests [2]. In CF, the system uses other users' ratings to items to understand what the user would like or dislike [3]. The CF can give recommendations to the user that is different from what the user had seen before [4].…”

Section: Introductionmentioning

confidence: 99%