A novel deep multi-criteria collaborative filtering model for recommendation system

Nassar, Nour; Jafar, Assef; Rahhal, Yasser

doi:10.1016/j.knosys.2019.06.019

Cited by 194 publications

(84 citation statements)

References 18 publications

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“…There are some other approaches that provide neural networks based on improved information coming from data, such as [29], where authors extract interaction behavior from bipartite graphs. Deep-learning architectures can be designed by extracting multi-criteria information from data [30]. Additionally, a classification-based deep learning collaborative filtering approach is stated in [31], where the learning process takes as information two different binary sources: (a) relevant/non-relevant votes, and (b) voted/non-voted items.…”

Section: Of 14mentioning

confidence: 99%

Deep Learning Architecture for Collaborative Filtering Recommender Systems

2020

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This paper provides an innovative deep learning architecture to improve collaborative filtering results in recommender systems. It exploits the potential of the reliability concept to raise predictions and recommendations quality by incorporating prediction errors (reliabilities) in the deep learning layers. The underlying idea is to recommend highly predicted items that also have been found as reliable ones. We use the deep learning architecture to extract the existing non-linear relations between predictions, reliabilities, and accurate recommendations. The proposed architecture consists of three related stages, providing three stacked abstraction levels: (a) real prediction errors, (b) predicted errors (reliabilities), and (c) predicted ratings (predictions). In turn, each abstraction level requires a learning process: (a) Matrix Factorization from ratings, (b) Multilayer Neural Network fed with real prediction errors and hidden factors, and (c) Multilayer Neural Network fed with reliabilities and hidden factors. A complete set of experiments has been run involving three representative and open datasets and a state-of-the-art baseline. The results show strong prediction improvements and also important recommendation improvements, particularly for the recall quality measure.

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Section: Of 14mentioning

confidence: 99%

Deep Learning Architecture for Collaborative Filtering Recommender Systems

2020

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“…At present, many model-based CF algorithms have been put forward, where matrix decomposition has gradually become the mainstream method in modelbased CF algorithms by virtue of transformation of high-dimensional sparse user rating data and excellent extensibility [12]. The main idea is to decompose the original user-commodity rating matrix into low-rank potential matrix through technologies like singular value decomposition and then obtain the prediction result through an analysis [15]. Matrix decomposition method has relieved the sparsity problem of CF algorithm to a certain degree, but cold start problem remains to be solved, so it is impossible to predict the probability for user to purchase new commodities with difficult similarity calculation [16,17].…”

Section: Collaborative Filtering Algorithmmentioning

confidence: 99%

Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms

Zhang

Dong

2020

J Wireless Com Network

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With the rapid development of wireless communication network, M-Commerce has achieved great success. Users leave a lot of historical behavior data when shopping on the M-Commerce platform. Using these data to predict future purchasing behaviors of the users will be of great significance for improving user experience and realizing mutual benefit and win-win result between merchant and user. Therefore, a sample balance-based multi-perspective feature ensemble learning was proposed in this study as the solution to predicting user purchasing behaviors, so as to acquire user’s historical purchasing behavioral data with sample balance. Influence feature of user purchasing behaviors was extracted from three perspectives—user, commodity and interaction, in order to further enrich the feature dimensions. Meanwhile, feature selection was carried out using XGBSFS algorithm. Large-scale real datasets were experimented on Alibaba M-Commerce platform. The experimental results show that the proposed method has achieved better prediction effect in various evaluation indexes such as precision and recall rate.

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“…At present many model-based CF algorithms have been put forward, where matrix decomposition has gradually become the mainstream method in model-based CF algorithms by virtue of transformation of highdimensional sparse user rating data and excellent extensibility [12]. The main idea is to decompose the original user-commodity rating matrix into low-rank potential matrix through technologies like singular value decomposition and then obtain the prediction result through an analysis [15]. Matrix decomposition method has relieved the sparsity problem of CF algorithm to a certain degree, but cold start problem remains to be solved, so it is impossible to predict the probability for user to purchase new commodities with difficult similarity calculation [16,17].…”

Section: Related Work 21 Collaborative Filtering Algorithmmentioning

confidence: 99%

Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms

Zhang

Dong

2020

Preprint

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With the rapid development of wireless communication network, M-Commerce has achieved great success. Users leave a lot of historical behavior data when shopping on the M-Commerce platform. Using these data to predict future purchasing behaviors of the users will be of great significance for improving user experience and realizing mutual benefit and win-win result between merchant and user. Therefore, a sample balance-based multi-perspective feature ensemble learning was proposed in this study as the solution to predicting user purchasing behaviors, so as to acquire user’s historical purchasing behavioral data with sample balance. Influence feature of user purchasing behaviors were extracted from three perspectives—user, commodity and interaction, in order to further enrich the feature dimensions. Meanwhile, feature selection was carried out using XGBSFS algorithm. Large-scale real datasets were experimented on Alibaba M-Commerce platform. The experimental results show that the proposed method has achieved better prediction effect in various evaluation indexes such as precision and recall rate.

show abstract

A novel deep multi-criteria collaborative filtering model for recommendation system

Cited by 194 publications

References 18 publications

Deep Learning Architecture for Collaborative Filtering Recommender Systems

Deep Learning Architecture for Collaborative Filtering Recommender Systems

Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms

Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms

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