Sparse General Non-Negative Matrix Factorization Based on Left Semi-Tensor Product

Chen, Chuan; Li, Lixiang; Peng, Haipeng; Liu, Yuhong; Zhu, Haihua; Yang, Yixian

doi:10.1109/access.2019.2924140

Cited by 11 publications

(4 citation statements)

References 33 publications

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“…From equations (13) to (16) we obtain the input X' and output Y' values to feed the neural network training. This is the proposed long model to arrange data in order to make RS classification and then RS recommendation.…”

Section: B Formalization and Data-toy Examplementioning

confidence: 99%

“…Matrix Factorization (MF) is the most implemented approach, since it provides accurate recommendations, it is easy to understand, and it obtains a good performance. There are several MF variations such as PMF [13] [14], BNMF [15], BPR [16] and eALS [17]. MF extracts the complex relations between items and users and codes them into a reduced number of hidden factors.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems

Bobadilla¹,

Ortega²,

Gutiérrez³

et al. 2020

IJIMAI

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This paper proposes a scalable and original classification-based deep neural architecture. Its collaborative filtering approach can be generalized to most of the existing recommender systems, since it just operates on the ratings dataset. The learning process is based on the binary relevant/non-relevant vote and the binary voted/ non-voted item information. This data reduction provides a new level of abstraction and it makes possible to design the classification-based architecture. In addition to the original architecture, its prediction process has a novel approach: it does not need to make a large number of predictions to get recommendations. Instead to run forward the neural network for each prediction, our approach runs forward the neural network just once to get a set of probabilities in its categorical output layer. The proposed neural architecture has been tested by using the MovieLens and FilmTrust datasets. A state-of-the-art baseline that outperforms current competitive approaches has been used. Results show a competitive recommendation quality and an interesting quality improvement on large number of recommendations, consistent with the architecture design. The architecture originality makes it possible to address a broad range of future works.

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Section: B Formalization and Data-toy Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems

Bobadilla¹,

Ortega²,

Gutiérrez³

et al. 2020

IJIMAI

View full text Add to dashboard Cite

show abstract

“…Predictions to each user can be computed by making the dot product of the user and the items factors. Well known MF variations are Positive Matrix Factorization (PMF) [16], [17], Bayesian Nonnegative Matrix Factorization (BNMF) [18], and Elementwise Alternating Least Squares (eALS) [19]. Finally, state of the art CF RS implementations make use of neural networks [76], some of them joining MF and the Multilayer Perceptron [20].…”

Section: A Recommendations To Individual Usersmentioning

confidence: 99%

“…Let σ f be the set of factors variances (14) Let be the factor f variance (15) Let θ be the threshold of required accumulated variance (16) Let T be the set of factors that hold the required accumulated variance (17) To fix the proposed method concepts we provide a data toy example. Fig.…”

mentioning

confidence: 99%

A Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups

Bobadilla¹,

Gutiérrez²,

Alonso³

et al. 2020

IJIMAI

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In the collaborative filtering recommender systems (CFRS) field, recommendation to group of users is mainly focused on stablished, occasional or random groups. These groups have a little number of users: relatives, friends, colleagues, etc. Our proposal deals with large numbers of automatically detected groups. Marketing and electronic commerce are typical targets of large homogenous groups. Large groups present a major difficulty in terms of automatically achieving homogeneity, equilibrated size and accurate recommendations. We provide a method that combines diverse machine learning algorithms in an original way: homogeneous groups are detected by means of a clustering based on hidden factors instead of ratings. Predictions are made using a virtual user model, and virtual users are obtained by performing a hidden factors aggregation. Additionally, this paper selects the most appropriate dimensionality reduction for the explained RS aim. We conduct a set of experiments to catch the maximum cumulative deviation of the ratings information. Results show an improvement on recommendations made to large homogeneous groups. It is also shown the desirability of designing specific methods and algorithms to deal with automatically detected groups.

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Statistical local descriptors for face recognition: a comprehensive study

Eleyan

2023

Multimed Tools Appl

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Sparse General Non-Negative Matrix Factorization Based on Left Semi-Tensor Product

Cited by 11 publications

References 33 publications

Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems

Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems

A Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups

Statistical local descriptors for face recognition: a comprehensive study

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