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collaborative filtering Based on Dual Conditional Restricted Boltzmann Machines 36

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Collaborative Filtering Based on Dual Conditional Restricted...

collaborative filtering Motivated Automatic Photo Tagging 36

第36届中国控制会议

作者： Chunchun Li Jun Li School of Information Science and Technology University of Science and Technology of China

ISBN: (纸本)9781538629185

The two-layer restricted Boltzmann machine（RBM） for rating prediction problems in recommendation systems has become one of the significant researches.A dual conditional restricted Boltzmann machine（dCRBM） model is proposed in this *** the dCRBM model,the training process uses rated/unrated ***,we also utilize the dual patterns of users and items,and build two CRBMs based on users and items ***,rating predictions of these two CRBMs via regression models are *** experimental results from MovieLens dataset show that the dCRBM model proposed in this paper helps to improve the prediction accuracy of the recommendation system.

关键词： Recommendation system conditional restricted Boltzmann machine collaborative filtering regression

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Collaborative Filtering Motivated Automatic Photo Tagging

A new item-based deep network structure using a restricted Boltzmann machine for collaborative filtering

第36届中国控制会议

作者： Yuan Zhou Siyu Xia Junkang Zhang Dandi Chen Key Laboratory of Measurement and Control of CSE School of AutomationSoutheast University Computer Science and Engineering Department University of Connecticut

ISBN: (纸本)9781538629185

Personal photographs shared in social network websites show a wide range of variations in illumination,pose and *** a result,conventional face recognition methods show poor performance in such uncontrolled *** resolve this problem,we explore incorporating associated social network context into visual face recognition *** by collaborative filtering technique,a new method for photo tagging task is proposed in this ***,a large-scale social network image dataset（SNRR-collection） is collected in this *** evaluated on SNRR-collection dataset demonstrate the effectiveness of our proposed model.

关键词： Photo tagging Face recognition Social Networks collaborative filtering Recommender system

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Frontiers of Information Technology & Electronic Engineering 2017年第5期18卷 658-666页

作者： Yong-ping DU Chang-qing YAO Shu-hua HUO Jing-xuan LIU Institute of Computer Science Beijing University of TechnologyBeijing 100124China Institute of Scientific and Technical information of China Beijing 100038China

The collaborative filtering(CF) technique has been widely used recently in recommendation systems. It needs historical data to give predictions. However, the data sparsity problem still exists. We propose a new item-based restricted Boltzmann machine(RBM) approach for CF and use the deep multilayer RBM network structure, which alleviates the data sparsity problem and has excellent ability to extract features. Each item is treated as a single RBM, and different items share the same weights and biases. The parameters are learned layer by layer in the deep network. The batch gradient descent algorithm with minibatch is used to increase the convergence speed. The new feature vector discovered by the multilayer RBM network structure is very effective in predicting a rating and achieves a better result. Experimental results on the data set of MovieL ens show that the item-based multilayer RBM approach achieves the best performance, with a mean absolute error of 0.6424 and a root-mean-square error of 0.7843.

关键词： Restricted Boltzmann machine Deep network structure collaborative filtering Recommendation system

维普期刊数据库

Attribute Reduction with Rough Set in Context-Aware collaborative filtering

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Chinese Journal of Electronics 2017年第5期26卷 973-980页

作者： HE Ming REN Wanpeng College of Computer Science Beijing University of Technology

The problem of different contextual information to influence the user-item-context interactions at varying degrees in context-aware recommender systems is *** improve the performance accuracy,we develop a novel attribute reduction algorithm in order to effectively extract the core contextual information using rough *** combine collaborative filtering with contextual information significance to generate more accurate *** experimentally evaluate our approach on UCI machine learning repository and two real world data *** results demonstrate that our proposed approach outperforms existing state-of-theart context-aware recommendation methods.

关键词： Context-aware recommendation systems(CARS) collaborative filtering Rough set

Improve the collaborative filtering Recommender System Performance by Trust Network Construction

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Chinese Journal of Electronics 2016年第3期25卷 418-423页

作者： DU Yongping DU Xiaoyan HUANG Liang College of Computer Science Beijing University of Technology

Data sparseness brings significant challenges to the research of recommender systems. It becomes more severe for neighborhood-based collaborative *** introduce the trust relation computing of the sociology field. Instead of the traditional similarity computing method, the trust degree is integrated for the nearest neighbor selection. The trust network is constructed by the expansion of different path length, and the trust value between the users can be obtained by the trust transmission rules. To verify the effectiveness of our method, we give the experiments on different techniques for rating prediction,including Pearson based method, the User position similarity(UPS) based method and the trust with Pearson and UPS. We also give the t-test result. The implementation of the experiment on the Epinions data set shows that the proposed method can improve the system performance significantly.

关键词： collaborative filtering Trust computing Recommender system

LECF: recommendation via learnable edge collaborative filtering

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Science China(Information Sciences) 2022年第1期65卷 116-130页

作者： Shitao XIAO Yingxia SHAO Yawen LI Hongzhi YIN Yanyan SHEN Bin CUI School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing Key Lab of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications School of Information Technology and Electrical Engineering The University of Queensland Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Computer Science and Technology Peking University National Engineering Laboratory for Big Data Analysis and Applications Peking University

The core of recommendation models is estimating the probability that a user will like an item based on historical interactions. Existing collaborative filtering(CF) algorithms compute the likelihood by utilizing simple relationships between objects, e.g., user-item, item-item, or user-user. They always rely on a single type of object-object relationship, ignoring other useful relationship information in data. In this paper, we model an interaction between user and item as an edge and propose a novel CF framework, called learnable edge collaborative filtering(LECF). LECF predicts the existence probability of an edge based on the connections among edges and is able to capture the complex relationship in data. Specifically, we first adopt the concept of line graph where each node represents an interaction edge; then calculate a weighted sum of similarity between the query edge and the observed edges(i.e., historical interactions) that are selected from the neighborhood of query edge in the line graph for a recommendation. In addition, we design an efficient propagation algorithm to speed up the training and inference of LECF. Extensive experiments on four public datasets demonstrate LECF can achieve better performance than the state-of-the-art methods.

关键词： collaborative filtering recommender system implicit feedback edge-edge similarity line graph

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Potential friendship discovery in social networks based on hybrid ensemble multiple collaborative filtering models in a 5G network environment

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Digital Communications and Networks 2022年第6期8卷 868-876页

作者： Hexuan Hu Zhenzhou Lin Qiang Hu Ye Zhang Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai UniversityNanjingChina College of Computer and Information Hohai UniversityNanjingChina Office of Teaching Affairs Nanjing University of Finance and EconomicsNanjingChina Electric Engineering College Tibet Agriculture&Animal Husbandry UniversityTibetChina

At present, 5G network technology is being applied to various social network modes, and it can provide technical and traffic support for social networks. Potential friendship discovery technology in 5G-enabled social networks is beneficial for users to make potential friends and expand their range of activities and social hierarchy, which is highly sought after in today's social networks and has great economic and application value. However, the sparsity of the dominant user association dataset in 5G-enabled social networks and the limitations of traditional collaborative filtering algorithms are two major challenges for the friend recommendation problem. Therefore, in order to overcome these problems regarding previous models, we propose a Hybrid Ensemble Multiple collaborative filtering Model (HEMCF) for discovering potential buddy relationships. The HEMCF model draws on a special autoencoder method that can effectively exploit the association matrix between friends and additional information to extract a hidden representation of users containing global structural information. Then, it uses the random walk-based graph embedding algorithm DeepWalk to extract another hidden representation of users in the buddy network containing local structural information. Finally, in the output module, the HEMCF model stacks and multiplies the two types of hidden representations of users to ensure that the information mentioned above is concentrated in the final output to generate the final prediction value. The magnitude of the prediction value represents the probability of the users being friends, with larger values representing a high probability of the two users being friends, and vice versa. Experimental results show that the proposed method boosts the accuracy of the relationship prediction over baselines on 3 real-world public datasets dramatically.

关键词： 5G network Social network collaborative filtering Recommendation system Friendship discovering

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Multiple Auxiliary Information Based Deep Model for collaborative filtering

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Journal of Computer Science & Technology 2018年第4期33卷 668-681页

作者： Lin Yue Xiao-Xin Sun Wen-Zhu Gao Guo-Zhong Feng Bang-Zuo Zhang School of Information Science and Technology Northeast Normal University Changchun 130117 China Key Laboratory of Applied Statistics of Ministry of Education Northeast Normal University Changchun 130024 China School of Environment Northeast Normal University Changchun 130117 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

With the ever-growing dynamicity, complexity, technique is proposed and becomes one of the most effective and volume of information resources, the recommendation techniques for solving the so-called problem of information overload. Traditional recommendation algorithms, such as collaborative filtering based on the user or item, only measure the degree of similarity between users or items with single criterion, i.e., ratings. According to the experience of previous studies, single criterion cannot accurately measure the similarity between user preferences or items. In recent years, the application of deep learning techniques has gained significant momentum in recommender systems for better understanding of user preferences, item characteristics, and historical interactions. In this work, we integrate plot information as auxiliary information into the denoising autoencoder （DAE）, called SemRe-DCF, which aims at learning semantic representations of item descriptions and succeeds in capturing fine-grained semantic regularities by using vector arithmetic to get better rating prediction. The results manifest that the proposed method can effectively improve the accuracy of prediction and solve the cold start problem.

关键词： semantic representation plot information denoising autoencoder collaborative filtering auxiliary information

Novel Positive Multi-Layer Graph Based Method for collaborative filtering Recommender Systems

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Journal of Computer Science & Technology 2022年第4期37卷 975-990页

作者： Bushra Alhijawi Ghazi AL-Naymat Department of Computer Science Princess Sumaya University for TechnologyAmman 11941Jordan Artificial Intelligence Research Center College of Engineering and Information TechnologyAjman UniversityAjman 20550U.A.E.

Recommender systems play an increasingly important role in a wide variety of applications to help users find favorite products. collaborative filtering has remarkable success in terms of accuracy and becomes one of the most popular recommendation methods. However, these methods have shown unpretentious performance in terms of novelty, diversity, and coverage. We propose a novel graph-based collaborative filtering method, namely Positive Multi-Layer Graph-Based Recommender System (PMLG-RS). PMLG-RS involves a positive multi-layer graph and a path search algorithm to generate recommendations. The positive multi-layer graph consists of two connected layers: the user and item layers. PMLG-RS requires developing a new path search method that finds the shortest path with the highest cost from a source node to every other node. A set of experiments are conducted to compare the PMLG-RS with well-known recommendation methods based on three benchmark datasets, MovieLens-100K, MovieLens-Last, and Film Trust. The results demonstrate the superiority of PMLG-RS and its high capability in making relevant, novel, and diverse recommendations for users.

关键词： recommender system collaborative filtering graph theory path search novelty coverage diversity