A hybrid neural network approach to combine textual information and rating information for item recommendation

Liu, Donghua; Li, Jing; Du, Bo; Chang, Jun; Gao, Rong; Wu, Yujia

doi:10.1007/s10115-020-01528-2

Cited by 24 publications

(13 citation statements)

References 42 publications

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“…The behavior data generated in the process of literary intelligent translation text improvement is recorded through logs. The setting of behavior logs provides a scientific basis for real-time literary translation error improvement [11][12]. When the search module receives the conversion demand, it immediately puts the system into working state, launches vocabulary processing and feature search, implements basic meaning acquisition and subject content search for the vocabulary to be checked, ensures that the close answers are within the search range, and improves the efficiency of literary intelligent translation text improvement.…”

Section: Figure 1 General Framework For Improving Literary Intelligen...mentioning

confidence: 99%

A Generative Adversarial Networks Based Approach for Literary Translation

2023

Teh. vjesn.

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This study aims to solve the problem of mistranslation due to the fact that literary intelligent translation only stays at the stage of text description and elaboration and lacks relevant facts. Therefore, this paper puts forward an improvement method of literary intelligent translation text based on generation confrontation network. First, an adaptive literary intelligent translation mode is designed under the generation confrontation network, and then the data of literary intelligent translation text improvement is preprocessed, and the data mining of text improvement quality evaluation is carried out. According to the mining results, a literary intelligent translation text improvement quality evaluation model is constructed to evaluate the quality of literary intelligent translation text improvement. According to the quality results, this paper constructs the improvement model of literary intelligent translation text, designs the improvement process, and completes the research on the improvement method of literary intelligent translation text that generates confrontation network. The experimental results show that this method has better detection effect of mistranslation features, better stability of the improved method, accurate and reliable results, and can improve the literary literacy of students and teachers.

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Section: Figure 1 General Framework For Improving Literary Intelligen...mentioning

confidence: 99%

A Generative Adversarial Networks Based Approach for Literary Translation

2023

Teh. vjesn.

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“…This process involves applying filters to the textual embeddings to extract the most meaningful latent features of the text. These latent features act as a representation of the text that can then be propagated through a NN as with any numeric vector (Liu, Li, et al, 2021).…”

Section: Neural Networkmentioning

confidence: 99%

soMLier: a South African wine recommender system

Redelinghuys

2023

Journal of Wine Research

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Though several commercial wine recommender systems exist, they are largely tailored to consumers outside of South Africa (SA). Consequently, these systems are of limited use to novice wine consumers in SA.To address this, the aim of this research is to develop a system for South African consumers that yields high-quality wine recommendations, maximises the accuracy of predicted ratings for those recommendations and provides insights into why those suggestions were made. To achieve this, a hybrid system "soMLier" (pronounced "sommelier") is built in this thesis that makes use of two datasets.

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“…The objectives of the model are to identify the user's point of interest, recommending products/services based on the user's latent interests [11]. In this situation, recommender systems have emerged as an effective mechanism to provide personalized recommendation services, which can effectively alleviate the information overload problem [12,13].…”

Section: Introductionmentioning

confidence: 99%

Accelerated - Generic Gradient Descent for E-Commerce Recommender Systems

Suresh¹,

Belinda²

2021

INDJCSE

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The online product reviews play an important role in the field of e-commerce as rating of the product infers the preference for customers and they rely on them while making purchases. The main aim of providing an efficient recommender system is to gain useful information for user based on quality, worth and quantity of the product. The reviews given by the customers are used to extract the features of products that evaluate the similarity content reviewed by another customer. Therefore, the reviews written by the distinct persons would be similar, but with different words of expression shows difficulty during review differentiation. The developed model performed mining customer review for a set of manufactured goods and their characteristics are used as product features. The proposed Gated Recurrent Unit with Accelerated -Generic Gradient Descent (GRU-AGGD) technique extracts the relevant and sufficient features from the review to overcome the existing optimization problems. These relevant features are the summary of the product used to predict the rank using the users-attributes and users-items matrix and these two matrixes give rise to Non-Negative Matrix Factorization model to provide recommendations to user for the product. The experimental results of product recommender system are evaluated using the proposed GRU-AGGD that shows the improvement in terms of performance measures. The average MSE and RMSE values obtained for the proposed GRU-AGGD technique are 0.84 and 0.91 respectively, which are higher when compared to the Bayesian recommender models.

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A hybrid neural network approach to combine textual information and rating information for item recommendation

Cited by 24 publications

References 42 publications

A Generative Adversarial Networks Based Approach for Literary Translation

A Generative Adversarial Networks Based Approach for Literary Translation

soMLier: a South African wine recommender system

Accelerated - Generic Gradient Descent for E-Commerce Recommender Systems

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