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probabilistic matrix factorization with personalized differential privacy

KNOWLEDGE-BASED SYSTEMS 2019年 183卷 104864-000页

作者： Zhang, Shun Liu, Laixiang Chen, Zhili Zhong, Hong Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China Anhui Univ Anhui Engn Lab IoT Secur Technol Hefei 230601 Anhui Peoples R China

probabilistic matrix factorization (PMF) plays a crucial role in recommendation systems. It requires a large amount of user data (such as user shopping records and movie ratings) to predict personal preferences, and thereby provides users high-quality recommendation services, which expose the risk of leakage of user privacy. Differential privacy, as a provable privacy protection framework, has been applied widely to recommendation systems. It is common that different individuals have different levels of privacy requirements on items. However, traditional differential privacy can only provide a uniform level of privacy protection for all users. In this paper, we mainly propose a probabilistic matrix factorization recommendation scheme with personalized differential privacy (PDP-PMF). It aims to meet users' privacy requirements specified at the item-level instead of giving the same level of privacy guarantees for all. We then develop a modified sampling mechanism (with bounded differential privacy) for achieving PDP. We also perform a theoretical analysis of the PDP-PMF scheme and demonstrate the privacy of the PDP-PMF scheme. In addition, we implement our proposed probabilistic matrix factorization schemes both with traditional and with personalized differential privacy (DP-PMF, PDP-PMF). A series of experiments are performed on real datasets to demonstrate the superior performance of PDP-PMF in recommendation quality. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Personalized differential privacy Recommendation system probabilistic matrix factorization

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probabilistic matrix factorization Recommendation of Self-Attention Mechanism Convolutional Neural Networks With Item Auxiliary Information

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IEEE ACCESS 2020年 8卷 208311-208321页

作者： Zhang, Chenkun Wang, Cheng Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China

To solve the problem of data sparsity in recommendation systems, this paper proposes a probabilistic matrix factorization recommendation of self-attention mechanism convolutional neural networks with item auxiliary information. First, the self-attention mechanism is added to convolutional matrix factorization and a probabilistic matrix factorization model, based on a convolutional neural networks with self-attention mechanism, is proposed. Second, after integrating auxiliary information, such as item comment, item name, and item category, probabilistic matrix factorization, based on a self-attention mechanism convolutional neural networks, is used for recommendation. Adding the self-attention mechanism allows convolutional matrix factorization to capture the long-distance dependence between different components of the auxiliary information. Integrating the item comment, name, and category information alleviates the data sparsity of recommendation, and improves the accuracy of rating prediction. Experimental results on the MovieLens-1M and MovieLens-10M datasets show that the probabilistic matrix factorization recommendation of self-attention mechanism convolutional neural networks with item comment, name, and category information is superior to existing popular methods, in respect of root mean square error.

关键词： probabilistic logic Convolutional neural networks Root mean square Recommendation algorithm self-attention mechanism convolutional neural networks probabilistic matrix factorization item auxiliary information

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Location-Aware Service Recommendation With Enhanced probabilistic matrix factorization

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IEEE ACCESS 2018年 6卷 62815-62825页

作者： Yin, Yuyu Chen, Lu Xu, Yueshen Wan, Jian Hangzhou Dianzi Univ Sch Comp Hangzhou 310018 Zhejiang Peoples R China Minist Educ Key Lab Complex Syst Modeling & Simulat Hangzhou 310018 Zhejiang Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710126 Shaanxi Peoples R China Zhejiang Univ Sci & Technol Sch Informat & Elect Engn Hangzhou 310023 Zhejiang Peoples R China

Owing to the ever-growing popularity of mobile computing, a large number of services have been developed for a variety of users. Considering this, recommending useful services to users is an urgent problem that needs to be addressed. Collaborative filtering (CF) approaches have been successfully adopted for services recommendation. Nevertheless, the prediction accuracy of the existing CF approaches is likely to reduce due to many reasons, such as inability to use side information and high data sparsity, which further lead to low quality of services recommendation. In order to solve these problems, some model-based CF approaches have been proposed. In this paper, we propose a novel quality of service prediction approach based on probabilistic matrix factorization (PMF), which has the capability of incorporating network location (an important factor in mobile computing) and implicit associations among users and services. First, we propose a novel clustering method that is capable of utilizing network location to cluster users. Based on the clustering results, we further propose an enhanced PMF model. The proposed model also incorporates the implicit associations among users and services. In addition, our model incorporates the implicit relationships between the users and the services. We conducted experiments on one real-world data set, and the experimental results show that our model outperforms the compared methods.

关键词： Implicit association network location probabilistic matrix factorization QoS prediction services recommendation

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Edge data based trailer inception probabilistic matrix factorization for context-aware movie recommendation

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2022年第5期25卷 1863-1882页

作者： Chen, Honglong Li, Zhe Wang, Zhu Ni, Zhichen Li, Junjian Xu, Ge Aziz, Abdul Xia, Feng China Univ Petr Coll Control Sci & Engn Qingdao 266580 Peoples R China Minjiang Univ Coll Comp & Control Engn Fuzhou 350108 Peoples R China Dalian Univ Technol Sch Software Dalian 116620 Peoples R China Federat Univ Australia Sch Engn IT & Phys Sci Ballarat Vic 3353 Australia

The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data. The visual information in the movie trailer is a significant part of the movie recommender system. However, due to the complexity of visual information extraction, data sparsity cannot be remarkably alleviated by merely using the rough visual features to improve the rating prediction accuracy. Fortunately, the convolutional neural network can be used to extract the visual features precisely. Therefore, the end-to-end neural image caption (NIC) model can be utilized to obtain the textual information describing the visual features of movie trailers. This paper proposes a trailer inception probabilistic matrix factorization model called Ti-PMF, which combines NIC, recurrent convolutional neural network, and probabilistic matrix factorization models as the rating prediction model. We implement the proposed Ti-PMF model with extensive experiments on three real-world datasets to validate its effectiveness. The experimental results illustrate that the proposed Ti-PMF outperforms the existing ones.

关键词： Neural image caption probabilistic matrix factorization Recommender systems Visual feature extraction

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Graph Regularized probabilistic matrix factorization for Drug-Drug Interactions Prediction

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第5期27卷 2565-2574页

作者： Jain, Stuti Chouzenoux, Emilie Kumar, Kriti Majumdar, Angshul Univ Paris Saclay CVN Inria Saclay F-91190 Gif Sur Yvette France IIIT Delhi Dept ECE Delhi 110020 India

Co-administration of two or more drugs simultaneously can result in adverse drug reactions. Identifying drug-drug interactions (DDIs) is necessary, especially for drug development and for repurposing old drugs. DDI prediction can be viewed as a matrix completion task, for which matrix factorization (MF) appears as a suitable solution. This paper presents a novel Graph Regularized probabilistic matrix factorization (GRPMF) method, which incorporates expert knowledge through a novel graph-based regularization strategy within an MF framework. An efficient and sounded optimization algorithm is proposed to solve the resulting non-convex problem in an alternating fashion. The performance of the proposed method is evaluated through the DrugBank dataset, and comparisons are provided against state-of-the-art techniques. The results demonstrate the superior performance of GRPMF when compared to its counterparts.

关键词： Drugs Predictive models Symmetric matrices probabilistic logic Task analysis matrix decomposition Gaussian distribution matrix factorization probabilistic matrix factorization graph regularization drug-drug interaction prediction

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Collaborative recommendation algorithm based on probabilistic matrix factorization in probabilistic latent semantic analysis

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第7期78卷 8711-8722页

作者： Huang, Li Tan, Wenan Sun, Yong Nanjing Univ Aero & Astr Sch Comp Sci & Technol Nanjing 210016 Jiangsu Peoples R China Jiangsu Open Univ Sch Informat & Electromech Engn Nanjing 210017 Jiangsu Peoples R China Shanghai Second Polytech Univ Shanghai Sch Comp & Informat Engn Shanghai 210209 Peoples R China Chuzhou Univ Coll Geog Informat & Tourism Chuzhou 239000 Anhui Peoples R China

In order to effectively solve the problem of new items and obviously improve the accuracy of the recommended results, we proposed a collaborative recommendation algorithm based on improved probabilistic latent semantic model in this paper, which introduces popularity factor into probabilistic latent semantic analysis to derive probabilistic matrix factorization model. The core idea is to integrate the semantic knowledge into the recommendation process to overcome the shortcomings of the traditional recommendation algorithm. We introduced popularity factor to form a quintuple vector so as to understand user preference, and can integrate the probabilistic matrix factorization to solve the problem of data sparsity on basis of probabilistic Latent Semantic Analysis;then the probabilistic matrix factorization model is adopted to construct the weighted similarity function to compute the recommendation result. Experimental study on real-world data-sets demonstrates that our proposed method can outperform three state-of-the art methods in recommendation accuracy.

关键词： Collaborative recommendation probabilistic latent semantic analysis probabilistic matrix factorization Popularity factor Semantic knowledge

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Deep probabilistic matrix factorization Framework for Online Collaborative Filtering

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IEEE ACCESS 2019年 7卷 56117-56128页

作者： Li, Kangkang Zhou, Xiuze Lin, Fan Zeng, Wenhua Alterovitz, Gil Xiamen Univ Software Sch Xiamen 361005 Fujian Peoples R China Xiamen Univ Dept Automat Xiamen 361005 Fujian Peoples R China Harvard Med Sch Computat Hlth Informat Program Boston MA 02138 USA

As living data growing and evolving rapidly, traditional machine learning algorithms are hard to update models when dealing with new training data. When new data arrives, traditional collaborative filtering methods have to train their model from scratch. It is expensive for them to retrain a model and update their parameters. Compared with traditional collaborative filtering, the online collaborative filtering is effective to update the models instantly when new data arrives. But the cold start and data sparsity remain major problems for online collaborative filtering. In this paper, we try to utilize the convolutional neural network to extract user/item features from user/item side information to address these problems. First, we proposed a deep bias probabilistic matrix factorization (DBPMF) model by utilizing the convolutional neural network to extract latent user/item features and adding the bias into probabilistic matrix factorization to track user rating behavior and item popularity. Second, we constrain user-specific and item-specific feature vectors to further improve the performance of the DBPMF. Third, we update two models by an online learning algorithm. The extensive experiments for three datasets (MovieLens100K, MovieLens1M, and HetRec2011) show that our methods have a better performance than baseline approaches.

关键词： Deep learning deep learning-based recommender systems online collaborative filtering probabilistic matrix factorization

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Pairwise probabilistic matrix factorization for implicit feedback collaborative filtering

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NEUROCOMPUTING 2016年 204卷 17-25页

作者： Li, Gai Ou, Weihua Shunde Polytech Dept Elect & Informat Engn Foshan 528300 Peoples R China Guizhou Normal Univ Sch Math & Comp Sci Guiyang 550001 Peoples R China

Implicit feedback collaborative filtering has attracted a lot of attention in collaborative filtering, which is called one-class collaborative filtering (OCCF). However, the low recommendation accuracy and the high cost of previous methods impede its generalization in real scenarios. In this paper, we develop a new model named pairwise probabilistic matrix factorization (PPMF) by using the advantages of RankRLS. PPMF model takes RankRLS integrated with PMF (probabilistic matrix factorization) to learn the relative preference for items. Different from previous works, PPMF minimizes the average number of inversions in ranking rather than maximize the gaps of the binary predicted values for OCCF problem. Meanwhile, we propose to optimize the PPMF model by the pointwise stochastic gradient descent algorithm based on bootstrap sampling, which is more effective for parameter learning than the original optimization method used in previous works. Experiments on two datasets show that PPMF model achieves satisfactory performance and outperforms the state-of-the-art implicit feedback collaborative ranking models by using different evaluation metrics. (C) 2016 Elsevier B.V. All rights reserved.

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A hybrid recommendation approach using LDA and probabilistic matrix factorization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4-Sup期22卷 S8811-S8821页

作者： Cao, Yulin Li, Wenli Zheng, Dongxia Dalian Univ Technol Fac Econ & Management Dalian Liaoning Peoples R China Dalian Polytech Univ Sch Management Dalian Liaoning Peoples R China Dalian Neusoft Informat Univ Dept Software Engn Dalian Liaoning Peoples R China

Recommender systems provide users with suggestions and selections. Hybrid approaches which combine the neighborhood-based methods and the model-based methods have become popular when building collaborative filtering recommenders, but similarity is established between users/items only by rating information which is just numerical value and does not contain any semantic information, leading to the loss of flexibility. To address this problem, a probabilistic matrix factorization recommendation approach fusing neighborhood selection based on Latent Dirichlet Allocation is proposed. In the proposed approach, users' and items' neighbors are selected through users' interests distribution and items' attributes distribution. Then the approach incorporates similarity matrix into probabilistic matrix factorization to obtain users' feature vectors and items' feature vectors to make recommendations. The experimental results show that our approach is effective to improve recommendation performance and to solve data sparsity problem.

关键词： Recommender systems Collaborative filtering probabilistic matrix factorization Latent Dirichlet allocation

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A POI recommendation approach integrating social spatio-temporal information into probabilistic matrix factorization

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KNOWLEDGE AND INFORMATION SYSTEMS 2021年第1期63卷 65-85页

作者： Davtalab, Mehri Alesheikh, Ali Asghar KN Toosi Univ Technol Dept Geospatial Informat Syst Fac Geodesy & Geomat Engn Valiasr St Tehran *** Iran

In recent years, point of interest (POI) recommendation has gained increasing attention all over the world. POI recommendation plays an indispensable role in assisting people to find places they are likely to enjoy. The exploitation of POIs recommendation by existing models is inadequate due to implicit correlations among users and POIs and cold start problem. To overcome these problems, this work proposed a social spatio-temporal probabilistic matrix factorization (SSTPMF) model that exploits POI similarity and user similarity, which integrates different spaces including the social space, geographical space and POI category space in similarity modelling. In other words, this model proposes a multivariable inference approach for POI recommendation using latent similarity factors. The results obtained from two real data sets, Foursquare and Gowalla, show that taking POI correlation and user similarity into account can further improve recommendation performance. In addition, the experimental results show that the SSTPMF model performs better in alleviating the cold start problem than state-of-the-art methods in terms of normalized discount cumulative gain on both data sets.

关键词： Point of interest Location-based social network probabilistic matrix factorization

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