A novel collaborative filtering algorithm of machine learning by integrating restricted Boltzmann machine and trust information

Wu, Xiaojun; Yuan, Xiaojie; Duan, Chunyan; Wu, Jing

doi:10.1007/s00521-018-3509-y

Cited by 23 publications

(16 citation statements)

References 21 publications

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“…DL methods are employed in the proposed work to improve the performance of recommendations, including CNN and SDAE. The work performed by Salakhutdinov et al [15] includes a two-layered restricted Boltzmann machine for representing explicit ratings for user items [30]. This work was used to describe ratings for ordinal nature.…”

Section: Related Workmentioning

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: Related Workmentioning

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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“…In the case of missing data, the membership of the fuzzy set is 0.33, the intuition index is 0.17, and the non-membership degree of the fuzzy set is 0.5. Therefore, intuitionistic fuzzy reasoning is performed on the set of data [2,0,3], and the result is (0.33, 0.5). The same processing is performed on the data in Table 4 to obtain the intuitionistic fuzzy inference data shown in Table 5.…”

Section: Examplesmentioning

confidence: 99%

“…So far, collaborative filtering recommendation algorithms are continually being improved. To alleviate the impact of data sparseness and cold start on the recommendation, Wu et al [2] propose to combine the limited Boltzmann machine model and trust information to improve the performance of recommendation, where the trust information is the degree of trust between the target user and other users. In the recommendation process, the accuracy of the recommendation is improved by considering the trust degree of the target user to the recommendation opinion.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Collaborative Filtering Recommendation Algorithm Based on Intuitionistic Fuzzy Reasoning Under Missing Data

Zhang

Wang

2020

IEEE Access

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“…Recommendation systems are some of the most powerful methods for suggesting products to customers based on their interests and online purchases (Jonnalagedda et al, 2016;Lin, Li & Lian, 2020;Nilashi, bin Ibrahim & Ithnin, 2014;Nilashi et al, 2015;Zhang et al, 2020b). In terms of personalization of recommendations, one of the most prevalently used methods is collaborative filtering (CF) (Nilashi, bin Ibrahim & Ithnin, 2014;Sardianos, Ballas Papadatos & Varlamis, 2019;Nilashi et al, 2015;Wu et al, 2019). In CF, personalized prediction of products depends on the latent features of users in a rating matrix.…”

Section: Introductionmentioning

confidence: 99%

Latent based temporal optimization approach for improving the performance of collaborative filtering

Al-Hadi

Sharef

Sulaiman

et al. 2020

PeerJ Computer Science

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Recommendation systems suggest peculiar products to customers based on their past ratings, preferences, and interests. These systems typically utilize collaborative filtering (CF) to analyze customers’ ratings for products within the rating matrix. CF suffers from the sparsity problem because a large number of rating grades are not accurately determined. Various prediction approaches have been used to solve this problem by learning its latent and temporal factors. A few other challenges such as latent feedback learning, customers’ drifting interests, overfitting, and the popularity decay of products over time have also been addressed. Existing works have typically deployed either short or long temporal representation for addressing the recommendation system issues. Although each effort improves on the accuracy of its respective benchmark, an integrative solution that could address all the problems without trading off its accuracy is needed. Thus, this paper presents a Latent-based Temporal Optimization (LTO) approach to improve the prediction accuracy of CF by learning the past attitudes of users and their interests over time. Experimental results show that the LTO approach efficiently improves the prediction accuracy of CF compared to the benchmark schemes.

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A novel collaborative filtering algorithm of machine learning by integrating restricted Boltzmann machine and trust information

Cited by 23 publications

References 21 publications

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

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

Improvement of Collaborative Filtering Recommendation Algorithm Based on Intuitionistic Fuzzy Reasoning Under Missing Data

Latent based temporal optimization approach for improving the performance of collaborative filtering

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