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An item orientated recommendation algorithm from the multi-view perspective

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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User collaborative filtering recommendation algorithm based on adaptive parametric optimisation SSPSO

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2017年第6期8卷 580-592页

作者： Pan, Xiuqin Zhou, Wenmin Lu, Yong Li, Ruixiang Minzu Univ China Sch Informat Engn Beijing Peoples R China

recommendation algorithm is one of the hot issues in the field of computer science, and is widely used in many aspects. Various types of e-commerce systems and applications need to use recommendation system to support. Collaborative filtering recommendation algorithm has been widely used in e-commerce system for its high recommendation accuracy. In order to improve the performance of the process of clustering and selection of nearest neighbours in collaborative filtering, there are several optimisation proposals in this paper directly to the recommendation algorithm which is based on the adaptive parametric optimisation semi-supervised PSO clustering (APO_SSPSO). This paper uses MovieLens data sets to compare the performance of the proposed method and the traditional collaborative filtering recommendation algorithm. Simulation has proved that this recommendation algorithm has accuracy and effectiveness in enhancing the performance of user collaborative filtering recommendation system. And to some extent, the algorithm has minimised space consumption.

关键词： user collaborative filtering recommendation algorithm particle swarm optimisation PSO semi-supervised learning

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The Improved Collaborative Filtering recommendation algorithm Research

The Improved Collaborative Filtering Recommendation Algorith...

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2018 5th ERMI International Conference on Social Issues, Social Work and Social Sciences (ERMI-SSS 2018)

作者： Lisha Piao Yunnan University Dianchi College

With the fast growing of e-business at home and abroad and the development,e-commerce sites can provide people with a growing number of goods and *** to better meet the needs of consumers and consumer psychology,to enhance the effectiveness of the recommendation,is in the development of e-commerce site needs to deal with the main *** filtering is the most widely used nowadays recommendation system of personalized recommendation technology,growing attention for its various scholars and *** firstly briefly stated the general relevant collaborative filtering recommendation algorithm,and analyze the recommendation algorithm under multiple interest may not apply to users recommend related questions,this paper discusses the variety of interest in order to meet user based on the collaborative filtering recommendation algorithm,and with the aid of experimental simulation activities,its effectiveness is verified.

关键词： The improved Collaborative filtering recommendation algorithm

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A personalized recommendation algorithm based on the fusion of trust relation and time series 20

A personalized recommendation algorithm based on the fusion ...

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20th IEEE International Conference on Computational Science and Engineering (CSE) / 15th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC)

作者： Chen HongLi East China Jiaotong Univ Software Sch Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781538632215

In order to further improve the accuracy of personalized recommendation algorithm in social network, on the basis of summarizing the traditional recommendation algorithm, this paper introduces the social relationship between users, the trust propagation mechanism and time sequence information and user-item score matrix information are fused to the probability matrix decomposition model, a new personalized recommendation model TTSMF is established, the model learns the potential features of the user and the item, and consider the time factor, and handle trust relationship between users. Even if the user does not score on any item, it can also learn the user's feature vector by trusting relationship. Compared with existing algorithms, TTSMF algorithm can better solve the cold start problem and improve the accuracy of the algorithm. By analyzing the time complexity of the algorithm, the TTSMF algorithm can be easily extended to the application scenarios with large data sets.

关键词： Trust relationship Time sequence recommendation algorithm User-item score matrix

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Personalized recommendation algorithm based on LFM with QoS constraint 6

Personalized recommendation algorithm based on LFM with QoS ...

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IEEE 6th International Congress on Big Data (BigData Congress)

作者： Jin, Ying Zhang, Yi-wen Hefei Univ Dept Management Hefei 230601 Anhui Peoples R China Anhui Univ Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230039 Anhui Peoples R China

ISBN: (纸本)9781538619964

The rapid development of Internet technology has ushered in the era of information overload. How to pick out information with excellent quality and reduce unnecessary browsing time is a problem to be solved urgently. In order to recommend information that users might be interested in, this paper presents a new personalized recommendation algorithm with the quality of service (QoS) constraints based on latent factor model (LFM). Compared with the traditional recommendation algorithms, this algorithm is capable of effectively improving the recall rate, accuracy rate and coverage rate of the personalized recommendation system.

关键词： latent factor model (LFM) quality of service (QoS) constraint recommendation algorithm

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A recommendation algorithm Based on Density Peak Clustering and Key Users 13

A Recommendation Algorithm Based on Density Peak Clustering ...

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13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Wang, Pei-Pei Liu, Pei-Yu Wang, Ru Zhu, Zhen-Fang Shandong Normal Univ Sch Informat Sci & Engn Shandong Prov Key Lab Distributed Comp Software N Jinan Shandong Peoples R China Shandong Jiaotong Univ Sch Informat Sci & Elect Engn Jinan Shandong Peoples R China

ISBN: (纸本)9781538621653

At present, the research on recommendation algorithm is limited to how to improve the existing algorithm or design new algorithm, blindly pursue the accuracy and diversity of the recommended results, rarely study the importance of the individual user in the recommendation system, but study the importance of the individual user in the recommendation system can improve the recommendation efficiency and enhance the robustness of the recommendation system. Aiming at this problem, the key users determination method based on density peak clustering is proposed, which can effectively distinguish the key users of the recommendation system. Furthermore, the recommendation algorithm based on density peak clustering and key users is proposed. The proposed algorithm is efficient through experimental verification, which not only improves the recommendation efficiency, but also improves the recommendation accuracy and diversity.

关键词： density peak clustering key users recommendation efficiency recommendation algorithm

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recommendation algorithm of cloud computing system based on random walk algorithm and collaborative filtering model

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International Journal of Technology Management 2017年第3期 79-81页

作者： Feng Zhang Hua Ma Lei Peng Lanhua Zhang School of Medical Information Engineering Taishan Medical University Taian China 271016

The traditional collaborative filtering recommendation technology has some shortcomings in the large data environment. To solve this problem, a personalized recommendation method based on cloud computing technology is proposed. The large data set and recommendation computation are decomposed into parallel processing on multiple computers. A parallel recommendation engine based on Hadoop open source framework is established, and the effectiveness of the system is validated by learning recommendation on an English training platform. The experimental results show that the scalability of the recommender system can be greatly improved by using cloud computing technology to handle massive data in the cluster. On the basis of the comparison of traditional recommendation algorithms, combined with the advantages of cloud computing, a personalized recommendation system based on cloud computing is proposed.

关键词： Random walk algorithm collaborative filtering model cloud computing system recommendation algorithm

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High-end Equipment Requirement Generation Model Based on recommendation algorithm 36

High-end Equipment Requirement Generation Model Based on Rec...

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第36届中国控制会议

作者： Yuren Wang Zhiwei Yang Yanqing Ye Yajie Dou College of Information System and Management National University of Defense Technology

ISBN: (纸本)9781538629185

High-end equipment manufacturing plays a core role in the equipment manufacturing *** user requirements is crucial in the process of high-end equipment manufacturing.A requirement generation model for the high-end equipment manufacturing is proposed based on recommendation algorithm,which can recommend some requirements to assist users in validating and managing their ***,the idea of using a recommendation algorithm to analyze the user requirement is ***,an advanced collaborative filtering algorithm is illustrated,towards capturing the characteristics of the high-end equipment as well as addressing the shortcomings of traditional ***,by applying the algorithm,a requirement generation model is established,which can recommend a personalized design of the configuration for users according to their historical behavior *** users can choose from the designs to validate and change their ***,the proposed recommendation model is evaluated by an example of individualized aircraft manufacturing,which verifies its feasibility.

关键词： requirement generation high-end equipment recommendation algorithm design of configuration

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Hybrid recommendation algorithm Based on Trust Relationship and User Preference 7

Hybrid Recommendation Algorithm Based on Trust Relationship ...

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7th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC)

作者： Dong, Wu Yi, Cai Kai, Yang Lingnan Normal Univ Sch Informat Engn Zhanjiang Guangdong Peoples R China South China Univ Technol Sch Software Engn Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781509030255

Most current trust-based recommendation algorithms are only based on reviews of trust neighbors, and do not consider users' preference. To handle this limitation, we propose a hybrid recommendation algorithm in social network (HRSN). Our proposed method constructs a trust network based on explicit trust relationships of a target user, and then identifies an opinion leader from his/her trust neighbors who can affect the target user and give proper comments on items. The proposed method integrates the comments of opinion leaders and user preferences to predict ratings on target. To demonstrate the effects of the proposed method, we conduct experiments on Epinions dataset by comparing with TrustMF, SoReg, SoRec and SVD++. The results show that HRSN can achieve the lowest MAE, and outperforms SVD++, TrustMF, SoReg and SoRec 11.6%, 9.6%, 7.5% and 1.7% respectively, which suggests that HRSN can provide a better recommendation than other compared methods.

关键词： Social network recommendation algorithm Trust relationship User preferences

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A Tag-based recommendation algorithm Integrating Short-term and Long-term Interests of Users

A Tag-based Recommendation Algorithm Integrating Short-term ...

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2017 2nd International Conference on Software, Multimedia and Communication Engineering（SMCE 2017)

作者： Rui LI Xin ZHANG School of Information Science and Engineering Hunan University

.In the collaborative tagging system,tags of users contain rich information on personalized preference,and time stamps of users show their interest ***' interest has the varied timeliness,but the existing recommendation algorithm fusing time information only emphasizes the short-term interest of users but fails to dig into the long-term stable interest of users and thus presents the low recommendation *** this paper,we propose a tag-based recommendation algorithm integrating short-term and long-term interests of *** algorithm first builds the short-term and long-term interest characteristics of users based on tags according to the use frequency,time decay,life cycle and volatility;then it maps the characteristics to the resources tagged by the user to form a user-resource pseudo-scoring ***,it calculates the set of the nearest neighbors of user with the matrix to give *** verification on the delicious data set shows that the algorithm improves recommendation precision and diversity compared with other classical algorithms.

关键词： Tag-based recommendation algorithm Interests of users

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