An MLP-based representation of neural tensor networks for the RDF data models

Abedini, Farhad; Menhaj, Mohammad Bagher; Keyvanpour, Mohammad Reza

doi:10.1007/s00521-017-3053-1

Cited by 8 publications

(1 citation statement)

References 29 publications

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“…Mere vector interaction is not satisfying to see user-item interaction. To resolve this issue, MLP is used to study the interaction between user and module inert vectors [22,37], and we applied masked surfaces on the concatenated vector. ReLu [23] is used as an activation function to develop the architecture, and the tower pattern is used to represent a neural network architecture in which the base is the extensive one.…”

Section: Multilayer Perceptronmentioning

confidence: 99%

A deep learning-based hybrid model for recommendation generation and ranking

Sivaramakrishnan

Subramaniyaswamy

Viloria

et al. 2020

Neural Comput & Applic

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A recommender system plays a vital role in information filtering and retrieval, and its application is omnipresent in many domains. There are some drawbacks such as the cold-start and the data sparsity problems which affect the performance of the recommender model. Various studies help with drastically improving the performance of recommender systems via unique methods, such as the traditional way of performing matrix factorization (MF) and also applying deep learning (DL) techniques in recent years. By using DL in the recommender system, we can overcome the difficulties of collaborative filtering. DL now focuses mainly on modeling content descriptions, but those models ignore the main factor of user-item interaction. In the proposed hybrid Bayesian stacked auto-denoising encoder (HBSADE) model, it recognizes the latent interests of the user and analyzes contextual reviews that are performed through the MF method. The objective of the model is to identify the user's point of interest, recommending products/services based on the user's latent interests. The proposed two-stage novel hybrid deep learning-based collaborative filtering method explores the user's point of interest, captures the communications between items and users and provides better recommendations in a personalized way. We used a multilayer neural network to manipulate the nonlinearities between the user and item communication from data. Experiments were to prove that our HBSADE outperforms existing methodologies over Amazon-b and Book-Crossing datasets.

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Section: Multilayer Perceptronmentioning

confidence: 99%