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Collaborative filtering recommendation algorithm integrating time windows and rating predictions

APPLIED INTELLIGENCE 2019年第8期49卷 3146-3157页

作者： Zhang, Pengfei Zhang, Zhijun Tian, Tian Wang, Yigui Shandong Jianzhu Univ Sch Comp Sci & Technol Jinan 250001 Shandong Peoples R China

This paper describes a new collaborative filtering recommendation algorithm based on probability matrix factorization. The proposed algorithm decomposes the rating matrix into two nonnegative matrixes using a predictive rating model. After normalization processing, these two nonnegative matrixes provide useful probability semantics. The posterior distribution of the real part of the probability model is calculated by the variational inference method. Finally, the preferences for items that users have not rated can be predicted. The user-item rating matrix is supplemented by a preference prediction value, resulting in a dense rating matrix. Finally, time weighting is integrated into the rating matrix to construct the 3D user-item-time model, which gives the recommendation results. According to experiments using open Netflix, MovieLens, and Epinion datasets, the proposed algorithm is superior to several existing recommendation algorithms in terms of rating predictions and recommendation effects.

关键词： Probability matrix factorization Time weighting Probability model recommendation algorithm

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Dual Auto-Encoder Based Rating Prediction recommendation algorithm

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IEEE ACCESS 2022年 10卷 97289-97297页

作者： Xin, Gaowei Qin, Jiwei Song, Xiaoyuan Zheng, Jiong Xinjiang Univ Sch Informat Sci & Engn Urumqi 830046 Peoples R China Xinjiang Univ Key Lab Signal Detect & Proc Urumqi 830046 Xinjiang Uygur Peoples R China

Collaborative filtering is the most widely used method in recommendation algorithms, but it still faces the serious problem of data sparsity. Traditional collaborative filtering uses matrix decomposition to learn the latent features of users and items. As an extension model of matrix decomposition, Funk-SVD model has attracted wide attention due to its good scalability and easy implementation, but it is difficult to extract the latent features of users and items from sparse rating information because it essentially learns the linear relationship between users and items. To solve this problem, we propose a Dual auto-encoder based Rating Prediction recommendation algorithm (DRPRA) model. The DRPRA model uses the strong ability of deep learning in feature learning, which combines double auto-encoders with Funk-SVD. First, the auto-encoder captures the latent features of users and items respectively. Then, the Funk-SVD combines the user features with item features to reconstruct the rating matrix. After that, we minimize the error between original rating matrix and reconstructed rating matrix, and to alleviate the problem of data sparsity and improve the accuracy of rating prediction effectively. We conducted extensive experiments on Movielens-100K, Movie Tweeting-10k, and Film Trust datasets, and the results show that the rating prediction model based on dual auto-encoders has a superior recommendation performance.

关键词： Feature extraction Predictive models Prediction algorithms Representation learning Matrix decomposition Deep learning Data models recommendation algorithm auto-encoder Funk-SVD sparsity

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Accurate Item recommendation algorithm of itemrank based on tag and context information

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COMPUTER COMMUNICATIONS 2021年 176卷 282-289页

作者： Huang, Zhiliang Ma, Hong Wang, Shizhi Shen, Yuan Air Force Early Warning Acad Wuhan 430000 Peoples R China Huazhong Univ Sci & Technol Wuhan 430000 Peoples R China

The traditional itemrank recommendation algorithm only uses the two-dimensional relationship between user and item to achieve recommendation, without considering the important information (such as label information and context information) that plays an important role in the association between user and item, so the accuracy of recommendation needs to be improved. Therefore, in this paper, tag information and context information are integrated into the link relationship between user and item, and a user-context-item tag association graph is constructed. An itemrank recommendation algorithm is proposed by integrating tag and context information. Firstly, ap-ml-rbf-relm model is used to determine user labels, and then deep neural network is used to determine the relationship between tags. Finally, item and label are calculated The association weight between the signatures is used to implement the recommendation service for the target users. Experimental results on public datasets show that the proposed algorithm is better than the traditional recommendation algorithm in recommendation accuracy.

关键词： Tag Context ItemRank recommendation algorithm

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An item orientated recommendation algorithm from the multi-view perspective

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NEUROCOMPUTING 2017年 269卷 261-272页

作者： Hu, Qi-Ying Zhao, Zhi-Lin Wang, Chang-Dong Lai, Jian-Huang Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China Guangdong Key Lab Informat Secur Technol Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510006 Guangdong Peoples R China

In the traditional recommendation algorithms, items are recommended to users on the basis of users' preferences to improve selling efficiency, which however cannot always raise revenues for manufacturers of particular items. Assume that, a manufacturer has a limited budget for an item's advertisement, with this budget, it is only possible for him to market this item to limited users. How to select the most suitable users that will increase advertisement revenue? It seems to be an insurmountable problem to the existing recommendation algorithms. To address this issue, a new item orientated recommendation algorithm from the multi-view perspective is proposed in this paper. Different from the existing recommendation algorithms, this model provides the target items with the users that are the most possible to purchase them. The basic idea is to simultaneously calculate the relationships between items and the rating differences between users from a multi-view model in which the purchasing records of each user are regarded as a view and each record is seen as a node in a view. The experimental results show that our proposed method outperforms the state-of-the-art methods in the scenario of item orientated recommendation. (C) 2017 Elsevier B.V. All rights reserved.

关键词： recommendation algorithm Item orientated Multi-view learning

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Hybrid recommendation algorithm based on real-valued RBM and CNN

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第10期19卷 10673-10686页

作者： Wu, Jue Yang, Lei Yang, Fujun Zhang, Peihong Bai, Keqiang Southwest Univ Sci & Technol Sch Comp Sci & Technol Mianyang Sichuan Peoples R China China Aerodynam Res & Dev Ctr Computat Aerodynam Inst Mianyang Sichuan Peoples R China

With the unprecedented development of big data, it is becoming hard to get the valuable information hence, the recommendation system is becoming more and more popular. When the limited Boltzmann machine is used for collaborative filtering, only the scoring matrix is considered, and the influence of the item content, the user characteristics and the user evaluation content on the predicted score is not considered. To solve this problem, the modified hybrid recommendation algorithm based on Gaussian restricted Boltzmann machine is proposed in the paper. The user text information and the item text information are input to the embedding layer to change the text information into numerical vector. The convolutional neural network is used to get the latent feature vector of the text information. The latent vector is connected to rating vector to get the item and the user vector. The user vector and the item vector are fused together to get the user-item matrix which is input to the visual layer of Gaussian restricted Boltzmann Machine to predict the ratings. Some simulation experiments have been performed on the algorithm, and the results of the experiments proved that the algorithm is feasible.

关键词： convolutional neural network Gaussian restricted Boltzmann machine recommendation algorithm

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Network Representation Learning Enhanced recommendation algorithm

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IEEE ACCESS 2019年 7卷 61388-61399页

作者： Wang, Qiang Yu, Yonghong Gao, Haiyan Zhang, Li Cao, Yang Mao, Lin Dou, Kaiqi Ni, Wenye Nanjing Univ Posts & Telecommun Tongda Coll Yangzhou 225127 Jiangsu Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England

With the popularity of social network applications, more and more recommender systems utilize trust relationships to improve the performance of traditional recommendation algorithms. Social-network-based recommendation algorithms generally assume that users with trust relations usually share common interests. However, the performance of most of the existing social-network-based recommendation algorithms is limited by the coarse-grained and sparse trust relationships. In this paper, we propose a network representation learning enhanced recommendation algorithm. Specifically, we first adopt a network representation technique to embed social network into a low-dimensional space, and then utilize the low-dimensional representations of users to infer fine-grained and dense trust relationships between users. Finally, we integrate the fine-grained and dense trust relationships into the matrix factorization model to learn user and item latent feature vectors. The experimental results on real-world datasets show that our proposed approach outperforms traditional social-network-based recommendation algorithms.

关键词： Network representation learning recommendation algorithm matrix factorization social network

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An ecommerce recommendation algorithm based on link prediction

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ALEXANDRIA ENGINEERING JOURNAL 2022年第1期61卷 905-910页

作者： Liu, Guoguang Binzhou Univ Sch Econ & Management Binzhou 256600 Peoples R China

In the field of ecommerce, most recommendation algorithms are based on user-item bipartite graph network (BGN). But this kind of recommendation algorithm is severely lacking in accuracy and diversity. In this paper, a novel ecommerce recommendation algorithm is proposed based on BGN link prediction. Firstly, all the user-item data were imported into distance formula to calculate the similarity between the attributes. Then, the BGN was projected into a single-mode net-work (SMN), making it more efficient to extract potential links from the BGN. On this basis, the potential links were predicted based on similarity. Through experiments on real ecommerce data-sets, it was proved that our algorithm has a higher accuracy and coverage than typical recommen-dation algorithms. (c) 2021 THE AUTHOR. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.

关键词： recommendation algorithm Bipartite graph network (BGN) Link prediction Ecommerce

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A Privacy-Preserving Cross-Domain recommendation algorithm for Industrial IoT Devices

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 227-237页

作者： Yu, Xu Peng, Qinglong Lv, Hongwu Zhan, Dingjia Hu, Qiang Du, Junwei Gong, Dunwei China Univ Petr Qingdao Inst Software Qingdao 266580 Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China

recommendation algorithms have been initially applied on the online business platform of industrial Internet of Things (IoT) devices. However, traditional recommendation algorithms are often difficult to solve the data sparsity problem. In fact, online shoppers are often accompanied by consumption behavior of other heterogeneous products, so we combine the consumer behavior of other heterogeneous products in the auxiliary domain to improve the recommendation performance of industrial IoT devices in the target domain. Due to privacy-preserving requirements, the original scoring information of the auxiliary domain is often not allowed to be directly shared with the target domain. Therefore, we propose a Privacy-Preserving Cross-Domain recommendation algorithm for industrial IoT devices. First, the non-privacy preference features are extracted through the auxiliary domain scoring data. Next, the extracted preference features are fused with the target domain information. Extensive experiments have been conducted on the Amazon dataset to verify the effectiveness of our method.

关键词： Decoding Privacy Recommender systems Optimization Industrial Internet of Things Data mining Feature extraction Industry 40 industrial Internet of Things privacy-preserving recommendation algorithm

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miRTRS: A recommendation algorithm for Predicting miRNA Targets

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020年第3期17卷 1032-1041页

作者： Jiang, Hui Wang, Jianxin Li, Min Lan, Wei Wu, Fang-Xiang Pan, Yi Cent South Univ Sch Informat Sci & Engn Changsha 410083 Peoples R China Univ South China Sch Comp Sci & Technol Hengyang 421001 Peoples R China Univ Saskatchewan Div Biomed Engn Saskatoon SK S7N 5A9 Canada Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA

microRNAs (miRNAs) are small and important non-coding RNAs that regulate gene expression in transcriptional and post-transcriptional level by combining with their targets (genes). Predicting miRNA targets is an important problem in biological research. It is expensive and time-consuming to identify miRNA targets by using biological experiments. Many computational methods have been proposed to predict miRNA targets. In this study, we develop a novel method, named miRTRS, for predicting miRNA targets based on a recommendation algorithm. miRTRS can predict targets for an isolated (new) miRNA with miRNA sequence similarity, as well as isolated (new) targets for a miRNA with gene sequence similarity. Furthermore, when compared to supervised machine learning methods, miRTRS does not need to select negative samples. We use 10-fold cross validation and independent datasets to evaluate the performance of our method. We compared miRTRS with two most recently published methods for miRNA target prediction. The experimental results have shown that our method miRTRS outperforms competing prediction methods in terms of AUC and other evaluation metrics.

关键词： Prediction algorithms Bipartite graph Feature extraction Prediction methods Gene expression Software algorithms miRNA target recommendation algorithm bipartite graph

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Research on Singular Value Decomposition recommendation algorithm Based on Data Filling

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGIES AND SYSTEMS APPROACH 2023年第3期16卷 1-15页

作者： Liu, Yarong Huang, Feiyang Xie, Xiaolan Huang, Haibin Guilin Univ Technol Guangxi Key Lab Embedded Technol & Intelligent Sy Guilin Guangxi Peoples R China Guilin Univ Technol Sch Informat Sci & Engn Guilin Guangxi Peoples R China

In the era of big data, the problem of information overload has become increasingly prominent. recommendation systems are widely studied due to the problem. Due to the sparseness of data, the recommendation effect is not always ideal. To alleviate the problem of data sparsity, a singular value decomposition recommendation algorithm based on data filling is proposed. First, an improved Tanimoto similarity coefficient calculation method is proposed to calculate the similarity, and effective interpolation data is generated for the singular value decomposition model according to the proposed prediction formula. The experimental results show that when using the same dataset MovieLens100K, compared with several commonly used recommendation algorithms, the improved algorithm improves the prediction accuracy of the model, In the best case, RMSE is 10.1% lower than KNNBasic, 7.8% lower than Slope One algorithm, 6.9% lower than SVD algorithm, and 4.8% lower than SVD++ algorithm, verifying that this method can improve the recommendation quality.

关键词： Big Data Data Filling Data Sparse recommendation algorithm Similarity Calculation Singular Value Decomposition

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