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SQPMF: successive point of interest recommendation system based on probability matrix factorization

APPLIED INTELLIGENCE 2024年第1期54卷 680-700页

作者： Wang, Jie Huang, Zhiyi Liu, Zhaobin Dalian Maritime Univ Coll Informat & Sci Technol Dalian 116026 Peoples R China Univ Otago Dunedin 9054 New Zealand

With the development of Location-Based Social Networks, successive Point Of Interest (POI) recommendation systems have become a hot spot in the field of recommendation systems. Successive POI recommendation systems suggest to users new and interesting places to visit. However, in real-life POI recommendation, there are often a small number of users facing a huge number of POIs. Traditional successive POI recommendation methods are not capable to deal with the large sparse datasets, as they only consider the simple relationship between users and POIs. They do not use the context information of users and POIs, which can enable better recommendation results. To utilize the context information, this paper proposes a novel successive POI recommendation method, SQPMF, which integrates user personal preferences, user social relationships and POI transition relationships into the system for accurate recommendation of the next POI. Our experimental evaluation using three real-life datasets, Gowalla, Foursquare and Brightkite, shows that our method SQPMF consistently outperforms all state-of-the-art methods in recommendation of successive POIs. Compared with other methods, SQPMF improves Precision, Recall and F1-score by an average of at least 6.1%, 5.8% and 5.7% respectively on three publicly available datasets.

关键词： Successive POIs Recommendation system probability matrix factorization Location-based social networks

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A fusion probability matrix factorization framework for link prediction

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KNOWLEDGE-BASED SYSTEMS 2018年 159卷 72-85页

作者： Wang, Zhiqiang Liang, Jiye Li, Ru Shanxi Univ Sch Comp & Informat Technol Minist Educ Key Lab Computat Intelligence & Chinese Informat Taiyuan 030006 Shanxi Peoples R China

Link prediction is a fundamental research problem in network data analysis. Networks usually contain rich node to-node topological metrics and their effective use is crucial to solve the link prediction problem. Despite significant advances, the existing metric-based link prediction methods usually only consider one single topological metric and thus show some limitations in different types of networks;the existing matrix factorization-based models mainly focus on modeling the adjacent matrix of a network, and this is hard to ensure the modeling of those topological metrics that can play an important role in link prediction. This study develops effective approaches by fusing the adjacent matrix and some key topological metrics in a unified probability matrix factorization framework. In these approaches, we consider not only the symmetric metrics but also the asymmetric metrics which are usually not taken into consideration in the related work. In our probability matrix factorization framework, we first present two fusion models by fusing two kinds of metrics respectively, and based on the fusion models, we put forward the final fusion models which fuse the two kinds of metrics simultaneously. To verify the performance of all the fusion models, we conduct the experiments with six directed networks and six undirected ones, and the extensive experiments show that the proposed models provide impressive predicting performance for link prediction.

关键词： Network data analysis probability matrix factorization Link prediction Fusion model

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A Recommendation Algorithm Based on Belief Propagation and probability matrix factorization 18

A Recommendation Algorithm Based on Belief Propagation and P...

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18th IEEE International Conference on Pervasive Intelligent and Computing (PICom)

作者： Li, Juan Wang, Xiao-Feng Feng, Wan-Jing North Minzu Univ Dept Comp Sci & Engn Yinchuan 750021 Ningxia Peoples R China Ningxia Key Lab Intelligent Informat & Big Data P Yinchuan 750021 Ningxia Peoples R China

ISBN: (纸本)9781728166094

Collaborative filtering recommendation algorithm is the most important recommended application methods at present. The selection of users (items) similarity measurement methods in this method may affect the final recommendation effect. probability matrix factorization algorithm is one of the more and more methods applied in collaborative filtering algorithms. This paper proposes to convert the users (items) consumption network into a bipartite graph and use belief propagation algorithm to obtain fuzzy nearest neighbor set, improving users (items) similarity measure and incorporating it into probability matrix factorization. Experimental tests on the data set showed that the proposed method performance was good.

关键词： component bipartite graph belief propagation algorithm probability matrix factorization nearest neighbors

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Operation and Maintenance Recommendation Based on probability matrix factorization with Tags for Relay Protection Equipment

Operation and Maintenance Recommendation Based on Probabilit...

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Power System and Green Energy Conference (PSGEC)

作者： Zhang, Lie Yang, Guo-sheng Hu, Haiyan Ying, Li-ming Liu, Xuan Tian, Shu-sheng Zhu, Gui-qi China Elect Power Res Inst Relay Protect Res Inst Beijing Peoples R China Wuhan Univ Sch Elect & Automat Wuhan Peoples R China Hubei Elect Power Co Ltd State Grid China Equipment Management Dept Wuhan Peoples R China

ISBN: (纸本)9781728190952

The feature attribute set covering the device ontology, operation state, maintenance and other equipment life trajectory process is extracted, the relaying protection equipment tagging is established, and the personalized recommendation of equipment operation and maintenance is made, which can provide technical support for the operation and maintenance, maintenance and technical upgrading of the relaying protection equipment efficiently. This paper analyzes the basic attributes and behavior attributes of the relay protection equipment, and establishes the tagging system of the relay protection equipment. On this basis, a joint probability matrix factorization recommendation algorithm based on equipment tags nearest neighbor selection is proposed. By using the feature information of the equipment tag, the approximate feature vectors of the device and the item are established and combined into a joint probability matrix factorization. The algorithm based on neighborhood influence considers the relationship among devices, items and tags, weakens the sparsity of tag information and rating information, and improves the recommendation quality.

关键词： Relay Protection probability matrix factorization Tags Personalized Recommendation

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Rating prediction of recommended item based on review deep learning and rating probability matrix factorization

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ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 2022年第0期54卷

作者： Zhu, Zhiguo Yan, Mengru Deng, Xiaoyi Gao, Ming Dongbei Univ Finance & Econ Sch Management Sci & Engn Dalian 116025 Peoples R China Huaqiao Univ Business Sch Quanzhou 362021 Peoples R China Rutgers State Univ Rutgers Business Sch Newark NJ 07102 USA

With a sharp improvement in E-commerce and data, the precise rating prediction of recommended items under user preferences has been a hot research topic in the EC intelligence domain. The rating data matrix factorization based methods have been widely used in item rating predictions in e-commerce recommendation systems. However, "cold start" and "data sparsity" have seriously restricted the accuracy of such methods. In addition to the rating data, as side information, the massive reviews posted by users rich in semantic and emotional in-formation express user preferences and item characteristics, and will certainly improve the accuracy of the rating prediction. Accordingly, this paper combines the deep learning for the review text and the matrix factorization method for rating data to predict the rating of the recommended items accurately. Firstly, based on the Deep Learning methods, self-attention mechanism and bi-directional RNN (Recurrent Neural Network) with the core of GRU (Gated Recurrent Unit), the deep nonlinear features of users and items are learned from review texts. Then, these features are introduced as a prior mean into the classical rating-based probability matrix factorization model to obtain the latent factor vectors of users and items with the rating of the recommended item accurately predicted. Finally, adopting MSE and MAE as the indicators, the extensive experiments conducted on four real datasets verify that the proposed model TFRMF (Topical Features Regularized matrix factorization) performs better than other classical counterparts. The achievements of this work will provide powerful methods and decision supports for accurate and personalized e-commerce recommendation practices.

关键词： Precise recommendation Ratings and reviews Deep learning Bi-directional GRU network Self -attention mechanism probability matrix factorization

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Study on Tag, Trust and probability matrix factorization Based Social Network Recommendation

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2018年第5期12卷 2082-2102页

作者： Liu, Zhigang Zhong, Haidong Wuxi Inst Technol Control Technol Inst Wuxi 214121 Jiangsu Peoples R China Zhejiang Wanli Univ Logist & E Commerce Sch Ningbo 315100 Zhejiang Peoples R China

In recent years, social network related applications such as WeChat, Facebook, Twitter and so on, have attracted hundreds of millions of people to share their experience, plan or organize, and attend social events with friends. In these operations, plenty of valuable information is accumulated, which makes an innovative approach to explore users' preference and overcome challenges in traditional recommender systems. Based on the study of the existing social network recommendation methods, we find there is an abundant information that can be incorporated into probability matrix factorization (PMF) model to handle challenges such as data sparsity in many recommender systems. Therefore, the research put forward a unified social network recommendation framework that combine tags, trust between users, ratings with PMF. The uniformed method is based on three existing recommendation models (SoRecUser, SoRecItem and SoRec), and the complexity analysis indicates that our approach has good effectiveness and can be applied to large-scale datasets. Furthermore, experimental results on publicly available *** dataset show that our method outperforms the existing state-of-art social network recommendation approaches, measured by MAE and MRSE in different data sparse conditions.

关键词： Social network recommendation Social tag Social trust probability matrix factorization

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Missing Data Recovery Based on probability matrix factorization Algorithm with prior information fusion 20

Missing Data Recovery Based on Probability Matrix Factorizat...

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Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering

作者： Ge Liu Guosheng Rui Wenbiao Tian Liyao Wu Junyi Huang Key Laboratory on Signal & Information Processing of Shandong Province Naval Aviation University Yantai China College of Aeronautical Foundation Naval Aviation University Yantai China

ISBN: (纸本)9781450387811

In order to solve the problem that the existing methods had low accuracy when recovering serious missing multivariable time series, a missing data recovery method based on fused prior information was proposed. This method was derived from the probability matrix factorization algorithm, making full use of the correlation between multi-sensor data and the correlation between time series data. Firstly, the proposed method exploited the latent correlation between multi-sources, and constructed the approximate representation of sensor latent factor feature matrix. Then, the time series of same sensor was analyzed, and the approximate representation of time series latent factor was constructed based on similarity between time series data. Finally, the two approximate representations were unified in the framework of probability matrix factorization algorithm, the latent feature representation and PMF algorithm parameters were obtained by learning. Simulation results show that the algorithm can recover data effectively.

关键词： multivariable time series correlation data missing prior information probability matrix factorization

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Social recommendation model combining trust propagation and sequential behaviors

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APPLIED INTELLIGENCE 2015年第3期43卷 695-706页

作者： Zhang, Zhijun Liu, Hong Shandong Jianzhu Univ Sch Comp Sci & Technol Jinan 250101 Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China Shandong Prov Key Lab Novel Distributed Comp Soft Jinan 250014 Peoples R China

All types of recommender systems have been thoroughly explored and developed in industry and academia with the advent of online social networks. However, current studies ignore the trust relationships among users and the time sequence among items, which may affect the quality of recommendations. Three crucial challenges of recommender system are prediction quality, scalability, and data sparsity. In this paper, we explore a model-based approach for recommendation in social networks which employs matrix factorization techniques. Advancing previous work, we incorporate the mechanism of temporal information and trust relations into the model. Specifically, our method utilizes shared latent feature space to constrain the objective function, as well as considers the influence of time and user trust relations simultaneously. Experimental results on the public domain dataset show that our approach performs better than state-of-the-art methods, particularly for cold-start users. Moreover, the complexity analysis indicates that our approach can be easily extended to large datasets.

关键词： Recommender system Social network Trust relation Temporal information probability matrix factorization Social recommendation

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Recommendation algorithm of influence and trust relationship

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第11期81卷 15635-15652页

作者： Zhang Li Chen XiaoBo Jiangsu Univ Technol Sch Comp Engn Changzhou 213001 Jiangsu Peoples R China Peoples Bank China Changzhou City Ctr Branch Changzhou 213001 Jiangsu Peoples R China

The recommendation system recommends information and services to users by collecting and analyzing user behaviors. Many current studies have shown that recommendation algorithms that integrate social network information can effectively improve recommendation performance. Most of the existing social recommendation algorithms assume that the trust relationship between users is singular and homogeneous. These social recommendation algorithms generally ignore two problems: (i) in a network of trust relationships, each user has various friends and trust relationships, which have an impact on user ratings. (ii) each user with different social status, which influences also affects the ratings between users. Propose a social network recommendation algorithm (Social Strength Trust Recommendation Algorithm, SSTRA) in this paper. Firstly, the algorithm uses the different out-degree and in-degree relationships among different users to calculate the different trust strengths of each user in social networks;secondly, it calculates the social influence of different users through the social ranking algorithm (SocailRank);thirdly, it will be based on the trust strength relationship of social networks and the social influence of users are integrated into the probability matrix factorization model. This method can achieve the purpose of optimizing recommendation results. The experimental results compared on the CiaoDVD dataset show that: Compared with the SocialMF, SoRec, RSTE, PMF, and Trust algorithms, the average MAE has increased by 1.33%, 1.69%, 4.88%, 11.17% and 220.41%, and the average RMSE has increased by 1.47%, 1.9%, 5.06%, 7.27%, 217.55%. The experimental results compared on the Ciao dataset show that: Compared with the SocialMF, SoRec, RSTE, PMF, and Trust algorithms, the average MAE is increased by 4.83%, 5.05%, 1.96%, 5.58%, 143.39%, and the average RMSE is increased by 1.76%, 2.17%, 2.1%, 2.38%, 151.1%. Experimental results show that the algorithm has obvious advantages

关键词： Social network Social influence Trust relationship Social recommendation algorithm probability matrix factorization

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Recommendation Model Based on Probabilistic matrix factorization, Integrating User Trust Relationship, Interest Mining, and Item Correlation

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IEEE ACCESS 2022年 10卷 132315-132331页

作者： Han, Lifeng Chen, Li Shi, Xiaolong Northwest Univ Sch Informat Sci & Technol Xian 710127 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710126 Peoples R China

Personalized recommendation has gained widespread attention in the academic and industrial fields to minimize information overload and has produced good benefits. Current research shows that social recommendations that effectively utilize user trust relationships can solve data sparsity and cold start problems common in traditional collaborative filtering algorithms. However, existing social recommendation models have focused only on direct trust relationships between users and have ignored indirect trust relationships and item correlations. To address these problems, we propose a probabilistic matrix factorization-based recommendation model based on trust relationships, interest mining, and item correlation. The proposed recommendation model considers the direct and indirect trust relationships between users, the similarities in users' preferences for item attributes, and the correlations between items. Finally, the rating of the item is predicted by the target user and provides the target user with personalized item recommendations. We evaluate the recommendation performances of the proposed recommendation model on the FilmTrust and the CiaoDVD datasets and find that it alleviates the user's cold start problem and provides higher recommendation accuracy and diversity than popular algorithms.

关键词： Correlation relationship direct trust indirect trust heterogeneous network probability matrix factorization user interest

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