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recommender systems Based on Tensor Decomposition

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Computers, Materials & Continua 2021年第1期66卷 621-630页

作者： Zhoubao Sun Xiaodong Zhang Haoyuan Li Yan Xiao Haifeng Guo School of Information Engineering Nanjing Audit UniversityNanjing211815China School of Computing National University of Singapore117417Singapore Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of TechnologyNanjing211169China

recommender system is an effective tool to solve the problems of information *** traditional recommender systems,especially the collaborative filtering ones,only consider the two factors of users and *** social networks contain abundant social information,such as tags,places and *** show that the social information has a great impact on recommendation *** not only describe the characteristics of items,but also reflect the interests and characteristics of *** the traditional recommender systems cannot parse multi-dimensional information,in this paper,a tensor decomposition model based on tag regularization is proposed which incorporates social information to benefit recommender *** original Singular Value Decomposition(SVD)model is optimized by mining the co-occurrence and mutual exclusion of tags,and their features are constrained by the relationship between *** on real dataset show that the proposed algorithm achieves superior performance to existing algorithms.

关键词： recommender system social information tensor decomposition tag

Securing recommender systems Against Shilling Attacks Using Social-Based Clustering

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Journal of Computer Science & Technology 2013年第4期28卷 616-624页

作者：张响亮 Tak Man Desmond Lee Georgios Pitsilis King Abdullah University of Science and Technology Faculty of Science Technology and Communication University of Luxembourg

Abstract recommender systems （RS） have been found supportive and practical in e-commerce and been established as useful aiding services. Despite their great adoption in the user communities, RS are still vulnerable to unscrupulous producers who try to promote their products by shilling the systems. With the advent of social networks new sources of information have been made available which can potentially render RS more resistant to attacks. In this paper we explore the information provided in the form of social links with clustering for diminishing the impact of attacks. We propose two algorithms, CLUTR and WCLUTR, to combine clustering with ＂trust＂ among users. We demonstrate that CLuTR and WCLUTR enhance the robustness of RS by experimentally evaluating them on data from a public consumer recommender system ***.

关键词： shilling attack recommender system collaborative filtering social trust clustering

Recommendation over time: a probabilistic model of time-aware recommender systems

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Science China(Information Sciences) 2019年第11期62卷 81-94页

作者： Zuoquan LIN Hanxuan CHEN Department of Information Science School of Mathematical Sciences Peking University

In time-aware recommender systems, we have to consider the dynamic aspect of recommendation that is fond of new coming data. Usually, the recent data is more closely related to current recommendation tasks and the early data are useful to indicate overall measurements of the preferences. We propose a probabilistic model that uses the early data to generate the prior distribution and the recent data to capture the change of the states of both users and items in collaborative filtering systems. Our model is dynamic in the sense that it updates every time receiving new data. The time cost of every updating has a constant limit, which is suitable to deal with large scale data for online recommendation. Experiments on real datasets show the improvement performance of our model over the existing time-aware recommender systems.

关键词： recommender system collaborative filtering hidden Markov model precision cold start

Serendipity in recommender systems:A systematic Literature Review

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Journal of Computer Science & Technology 2021年第2期36卷 375-396页

作者： Reza Jafari Ziarani Reza Ravanmehr Department of Computer Engineering Central Tehran BranchIslamic Azad UniversityTehran ***Iran

A recommender system is employed to accurately recommend items,which are expected to attract the user's *** over-emphasis on the accuracy of the recommendations can cause information over-specialization and make recommendations boring and even *** and diversity are two partly useful solutions to these ***,novel and diverse recommendations cannot merely ensure that users are attracted since such recommendations may not be relevant to the user's ***,it is necessary to consider other criteria,such as unexpectedness and *** is a criterion for making appealing and useful *** usefulness of serendipitous recommendations is the main superiority of this criterion over novelty and *** bulk of studies of recommender systems have focused on serendipity in recent ***,a systematic literature review is conducted in this paper on previous studies of serendipity-oriented recommender ***,this paper focuses on the contextual convergence of serendipity definitions,datasets,serendipitous recommendation methods,and their evaluation ***,the trends and existing potentials of the serendipity-oriented recommender systems are discussed for future *** results of the systematic literature review present that the quality and the quantity of articles in the serendipity-oriented recommender systems are progressing.

关键词： systematic literature review recommender system serendipity evaluation metric evaluation method

Novel Positive Multi-Layer Graph Based Method for Collaborative Filtering recommender systems

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Journal of Computer Science & Technology 2022年第4期37卷 975-990页

作者： Bushra Alhijawi Ghazi AL-Naymat Department of Computer Science Princess Sumaya University for TechnologyAmman 11941Jordan Artificial Intelligence Research Center College of Engineering and Information TechnologyAjman UniversityAjman 20550U.A.E.

recommender systems play an increasingly important role in a wide variety of applications to help users find favorite products. Collaborative filtering has remarkable success in terms of accuracy and becomes one of the most popular recommendation methods. However, these methods have shown unpretentious performance in terms of novelty, diversity, and coverage. We propose a novel graph-based collaborative filtering method, namely Positive Multi-Layer Graph-Based recommender system (PMLG-RS). PMLG-RS involves a positive multi-layer graph and a path search algorithm to generate recommendations. The positive multi-layer graph consists of two connected layers: the user and item layers. PMLG-RS requires developing a new path search method that finds the shortest path with the highest cost from a source node to every other node. A set of experiments are conducted to compare the PMLG-RS with well-known recommendation methods based on three benchmark datasets, MovieLens-100K, MovieLens-Last, and Film Trust. The results demonstrate the superiority of PMLG-RS and its high capability in making relevant, novel, and diverse recommendations for users.

关键词： recommender system collaborative filtering graph theory path search novelty coverage diversity

Enhancing recommender systems by incorporating social information

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Journal of Zhejiang University-Science C(Computers and Electronics) 2013年第9期14卷 711-721页

作者： Li-wei HUANG Gui-sheng CHEN Yu-chao LIU De-yi LI Institute of Command Information system PLA University of Science and Technology Institute of Electronic system Engineering

Although recommendation techniques have achieved distinct developments over the decades,the data sparseness problem of the involved user-item matrix still seriously influences the recommendation *** of the existing techniques for recommender systems cannot easily deal with users who have very few *** to combine the increasing amount of different types of social information such as user generated content and social relationships to enhance the prediction precision of the recommender systems remains a huge *** this paper,based on a factor graph model,we formalize the problem in a semi-supervised probabilistic model,which can incorporate different user information,user relationships,and user-item ratings for learning to predict the unknown *** evaluate the method in two different genres of datasets,Douban and *** indicate that our method outperforms several state-of-the-art recommendation ***,a distributed learning algorithm is developed to scale up the approach to real large datasets.

关键词： recommender system Social information Factor graph model

Generalized Embedding Machines for recommender systems

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Machine Intelligence Research 2024年第3期21卷 571-584页

作者： Enneng Yang Xin Xin Li Shen Yudong Luo Guibing Guo Software College Northeastern UniversityShenyang110000China School of Computer Science and Technology Shandong UniversityQingdao266000China JD Explore Academy JD Explore AcademyBeijing100000China

Factorization machine (FM) is an effective model for feature-based recommendation that utilizes inner products to capture second-order feature interactions. However, one of the major drawbacks of FM is that it cannot capture complex high-order interaction signals. A common solution is to change the interaction function, such as stacking deep neural networks on the top level of FM. In this work, we propose an alternative approach to model high-order interaction signals at the embedding level, namely generalized embedding machine (GEM). The embedding used in GEM encodes not only the information from the feature itself but also the information from other correlated features. Under such a situation, the embedding becomes high-order. Then we can incorporate GEM with FM and even its advanced variants to perform feature interactions. More specifically, in this paper, we utilize graph convolution networks (GCN) to generate high-order embeddings. We integrate GEM with several FM-based models and conduct extensive experiments on two real-world datasets. The results demonstrate significant improvement of GEM over the corresponding baselines.

关键词： Feature interactions high-order interaction factorization machine(FM) recommender system graph neural network(GNN)

Customization Using Fuzzy recommender systems

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Journal of Donghua University(English Edition) 2004年第3期21卷 9-14页

作者： Ronald R. Yager Machine Intelligence Institute Iona College New Rochelle NY 10801

We discuss some methods for constructing recommender systems. An important feature of the methods studied here is that we assume the availability of a description, representation, of the objects being considered for recommendation. The approaches studied here differ from collaborative filtering in that we only use preferences information from the individual for whom we are providing the recommendation and make no use the preferences of other collaborators. We provide a detailed discussion of the construction of the representation schema used. We consider two sources of information about the users preferences. The first are direct statements about the type of objects the user likes. The second source of information comes from ratings of objects which the user has experienced.

关键词： recommender system fuzzy logic preference.

MapReduce based computation of the diffusion method in recommender systems

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High Technology Letters 2016年第3期22卷 288-296页

作者：彭飞 You Jiali Zeng Xuewen Deng Haojiang National Network New Media Engineering Research Center Institute of Acoustics Chinese Academy of Sciences Beijing 100190 P. R China University of Chinese Academy of Sciences Beijing 100049 P. R. China

The performance of existing diffusion-based algorithms in recommender systems is still limited by the processing ability of a single computer. In order to conduct the diffusion computation on large data sets,a parallel implementation of the classic diffusion method on the MapReduce framework is proposed. At first,the diffusion computation is transformed from a summation format to a cascade matrix multiplication format,and then,a parallel matrix multiplication algorithm based on dynamic vector is proposed to reduce the CPU and I / O cost on the MapReduce framework,which can also be applied to other parallel matrix multiplication scenarios. Then,block partitioning is used to further improve the performance,while the order of matrix multiplication is also taken into *** on different kinds of data sets have verified the efficiency of the proposed method.

关键词： MapReduce recommender system diffusion parallel matrix multiplication