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recommendation algorithm based on community structure and user trust

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第20期33卷 e6375-e6375页

作者： Lei, Guo Sheng, Yang Shaozi, Li Qingshou, Wu Wensen, Yu Wuyi Univ Sch Math & Comp Sci Wuyishan Peoples R China Fujian Prov Dev & Reform Commiss Digital Fujian Tourism Big Data Inst Wuyishan Peoples R China Fujian Prov Dept Educ Key Lab Cognit Comp & Intelligent Informat Proc Wuyishan Peoples R China Xiamen Univ Sch Informat Xiamen Peoples R China

While contemporary community-based recommendation algorithms based on a single community structure are more capable of processing large datasets than ever, they lack recommendation precision. This article proposes a collaborative filtering recommendation algorithm that integrates community structure and user implicit trust. The algorithm first applies a method based on the Gaussian function to fill the matrix of item ratings of users to alleviate data sparsity. It then uses the trust matrix to obtain the asymmetric trust relationship of the trustor and trustee, based on which the degree of users' implicit trust is calculated. The users are divided into communities based on the implicit trust degree to determine the influence among users more accurately. The algorithm then predicts the target user's rating using the ratings of users in the community to generate recommendations. To verify the performance of the proposed algorithm, we compared the proposed algorithm with three contemporary algorithms under the same conditions using FilmTrust datasets. The recommendation accuracy as well as the mean absolute error and root mean square error values of the proposed algorithm were better than those of the other four algorithms by approximately 14% and 4%, respectively. The experimental results demonstrate that the proposed algorithm can achieve better recommendation efficiency than existing algorithms.

关键词： community discovery recommendation algorithm social network trust relationship

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SNS Based recommendation algorithm

SNS Based Recommendation Algorithm

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4th International Conference on Information Science and Applications (ICISA)

作者： Yoo, So-Yeop Jeong, Ok-Ran Gachon Univ Dept Software Design & Management Songnam South Korea

ISBN: (纸本)9781479906048;9781479906024

Social network service (SNS) based recommendation system is playing an important role in searching information in the vast number of SNS community websites. In this paper, we propose a recommendation algorithm using intimacy information based on the SNS after extracting information on the social network having relationship with the user. It is an algorithm to recommend the contents with high relationship based on the list of friends who are predicted as actually having close relationship with the user according to the familiarity after extracting the list of friends within the SNS using SNS information of the user.

关键词： SNS (social network service) recommendation algorithm intimacy information

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Research on Context-Aware recommendation algorithm Based on RFID Application 5th

Research on Context-Aware Recommendation Algorithm Based on ...

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5th EAI International Conference on Advanced Hybrid Information Processing (ADHIP)

作者： Zhao, Yan Ningbo City Coll Vocat Technol Ningbo 315199 Peoples R China

ISBN: (纸本)9783030945541;9783030945534

With the development of mobile Internet, more and more users get information through mobile terminals. However, the current personalized recommendation system is lack of the ability to perceive the user's situation and provide personalized information recommendation service for users. For this reason, this paper proposes a context aware recommendation algorithm based on RFID application, which collects user resource category preference learning features by combining with context features, and integrates different category preferences for collaborative filtering personalized information recommendation. The RFID method is used to learn the user's preference for each resource category in different contexts, and then the category preference is combined with the traditional RFID context-aware recommendation algorithm to generate personalized information recommendations that are in line with the user's current context. Experiments show that the context-aware recommendation algorithm proposed in this paper based on RFID applications can improve the accuracy of recommendation.

关键词： RFID Situational awareness Information recommendation recommendation algorithm

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A Solution of Missing Value in Collaborative Filtering recommendation algorithm

A Solution of Missing Value in Collaborative Filtering Recom...

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Chinese Automation Congress (CAC)

作者： Jie Qin Lei Cao Hui Peng PLA Univ Sci & Technol Coll Command Informat Syst Nanjing 210007 Jiangsu Peoples R China

ISBN: (纸本)9781467371896

In order to solve the sparsity of User-Item scoring matrix in collaborative filtering recommendation, proposed an algorithm that combining user's interests with item's quality to calculate ungraded items in matrix. By setting the weight of user and item, synthesized the value of missing value, which were used to replace the ungraded value in scoring matrix to calculate similarity. Experiment shows that the algorithm can improve the recommendation effect, and when the user's weight values 0.4, MAE reaches minimum, and recommendation quality reaches maximum.

关键词： collaborative filtering sparsity recommendation algorithm MAE

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An Enhanced recommendation algorithm Based on Modified User-Based Collaborative Filtering 4

An Enhanced Recommendation Algorithm Based on Modified User-...

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4th IEEE International Conference on Computer and Communication Systems (ICCCS)

作者： Lumauag, Ramil G. Sison, Ariel M. Medina, Ruji P. Technol Inst Philippines Grad Sch Program Quezon City Philippines Emilio Aguinaldo Coll Sch Comp Studies Manila Philippines

ISBN: (纸本)9781728113227

With the huge amount of information available on the Internet, recommendation systems gained popularity over the years. Traditional recommendation algorithm usually uses collaborative filtering to determine user and item similarity. However, data sparsity and overfitting affects the accuracy of the recommendation systems that lead to poor recommendation quality. This paper presents an enhanced recommendation algorithm based on modified user-based collaborative filtering to overcome the problem and improve the recommendation quality. The enhanced algorithm was compared to the traditional algorithm using the MovieLens dataset and evaluates its accuracy and performance using the Root Mean Square Error (RSME), Precision and Recall. The experimental results show that the enhanced algorithm outperforms the traditional algorithm and improves the accuracy of the recommendation.

关键词： collaborative filtering algorithm recommendation algorithm recommender systems user-based collaborative filtering

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Improved Collaborative Filtering recommendation algorithm based on Weighted Association Rules

Improved Collaborative Filtering Recommendation Algorithm ba...

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2nd International Conference on Information Technology and Management Innovation (ICITMI 2013)

作者： Yang, Hai Shandong Jiaotong Univ Coll Informat Sci & Elect Engn Jinan 250023 Shandong Peoples R China

ISBN: (纸本)9783037858646

In this paper the collaborative filtering recommendation and association rules are introduced firstly. Aiming at the shortcomings of basic collaborative filtering recommendation, this paper proposes an improved collaborative filtering recommendation algorithm based on weighted association rules (CFRA-WAR). Finally the simulation experiments are carried out to verify the validity of the improved recommendation algorithm. The results of simulation experiments show that the recommendation accuracy of CFRA-WAR is superior to basic collaborative filtering recommendation algorithm, although the algorithm time of CFRA-WAR is a little longer.

关键词： recommendation algorithm collaborative filtering weighted association rules calculation of similarity

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Collaborative Filtering recommendation algorithm Classification and Comparative Study 2019

Collaborative Filtering Recommendation Algorithm Classificat...

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4th International Conference on Distance Education and Learning (ICDEL)

作者： Chao, Duan Lu Kaili Jing, Zhang Xie, Jerry Cent China Normal Univ Natl Engn Ctr E Learning Wuhan 430079 Peoples R China

ISBN: (纸本)9781450362658

With the rapid development of the information society, both the producers of teaching resources and students are facing enormous challenges. In this context, a recommendation system that can connect the producers and students of teaching resources to achieve a win-win situation is proposed. Collaborative filtering is currently the most successful recommendation technique. In this article the authors firstly introduced the collaborative filtering recommendation algorithm from the angle of user-based, project-based and model-based algorithms. Then they studied the current popular SVD and RBM recommendation algorithms mainly by comparing the experimental results of the basic project-based, SVD and RBM algorithms on the public data set movielens, and pointed out that the recommendation system will be a real-time, multi-modal trend in the age of educational informatization. The rapid development of education informatization has made MOOC, micro-classes and other teaching resources mainly relying on video resources to grow rapidly. The collaborative filtering recommendation algorithm expects to associate the most suitable high-quality resources with the most needed students.

关键词： recommendation algorithm collaborative filtering teaching resources classification comparative

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Research on the Application of Curriculum Knowledge Point recommendation algorithm Based on Learning Diagnosis Model 5

Research on the Application of Curriculum Knowledge Point Re...

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5th International Conference on Electromechanical Control Technology and Transportation (ICECTT)

作者： Gan, Baiqiang Zhang, Chi Guangzhou Nanyang Polytech Guangzhou 510900 Peoples R China Sun Yat Sen Univ Nanfang Coll Guangzhou 510900 Peoples R China

ISBN: (数字)9781728199283

ISBN: (纸本)9781728199283

Because the degree of mastering knowledge points in courses in traditional cognitive diagnostic models cannot be probabilistic, there are only two situations: mastery and non-mastery. Therefore, for the current research, the recommendations of knowledge points recommended by learners' learning behavior attributes are not fully considered to be insufficient, this paper proposes a curriculum knowledge point recommendation algorithm model based on learning diagnosis, the model comprehensively considers the learner's learning emotions, learner problem test conditions and knowledge point characteristics, and the film and television synthesis in the Chaoxing online teaching service platform the course learning data is tested to verify the effectiveness of the recommendation algorithm. The experimental results show that the effectiveness and accuracy of the recommendation algorithm model proposed in this paper can meet the learning needs of learners.

关键词： learning diagnosis model course knowledge points recommendation algorithm clustering algorithm collaborative filtering algorithm

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E-commerce recommendation algorithm Based on Multi-level Association Rules

E-commerce Recommendation Algorithm Based on Multi-level Ass...

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Conference on Electronic Commerce, Web Application and Communication

作者： Huang, ShuiYuan Duan, LongZhen Nanchang Univ Sch Informat Nanchang 330031 Peoples R China

ISBN: (纸本)9783642286544

With the rapid development of the E-commerce, recommendation system and algorithm has become the relevant research hotspot. In this paper, we proposed a new recommendation algorithm,based on Multi-level Association Rules (MAR). The algorithm improves the precision and individuation degree of the recommendation,and significantly reduces the time needed for the recommendation. It mines the rule of the customer's choice of commodity,by using multi-level association rule, builds a model for choice prediction.

关键词： recommendation algorithm Multi-level association E-commerce Mining

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Matrix Decomposition recommendation algorithm Based on Multiple Social Relationships 2

Matrix Decomposition Recommendation Algorithm Based on Multi...

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2nd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (IEEE ECBIOS) - Biomedical Engineering, Healthcare and Sustainability

作者： Gong, Cuijuan Sun, Gengxin Chen, Chi-Cheng Bin, Sheng Qingdao Univ Sch Data Sci & Software Engn 308 Ningxia Rd Qingdao Shandong Peoples R China

ISBN: (纸本)9781728187129

A real society has multiple social relationships between users, but existing social network recommendation algorithms often only introduce a social relationship into the recommendation system. This paper introduces a variety of social relationships into the recommendation system based on a multi-subnet composite complex network model. Based on the analysis of the experimental results on the Epinions dataset, a recommendation algorithm introducing multiple social relationships has a significantly higher recommendation accuracy than a recommendation algorithm.

关键词： multi-relational social network recommendation algorithm

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