期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到11篇文章
< 1 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
A Robust Collaborative Recommendation Algorithm Based on k-distance and Tukey M-estimator 认领 引用 被引量：6
1
作者 YI Huawei ZHANG Fuzhi LAN Jie 《China Communications》 SCIE CSCD 2014年第9期112-123,共12页
The existing collaborative recommendation algorithms have lower robustness against shilling attacks.With this problem in mind,in this paper we propose a robust collaborative recommendation algorithm based on k-distanc... The existing collaborative recommendation algorithms have lower robustness against shilling attacks.With this problem in mind,in this paper we propose a robust collaborative recommendation algorithm based on k-distance and Tukey M-estimator.Firstly,we propose a k-distancebased method to compute user suspicion degree(USD).The reliable neighbor model can be constructed through incorporating the user suspicion degree into user neighbor model.The influence of attack profiles on the recommendation results is reduced through adjusting similarities among users.Then,Tukey M-estimator is introduced to construct robust matrix factorization model,which can realize the robust estimation of user feature matrix and item feature matrix and reduce the influence of attack profiles on item feature matrix.Finally,a robust collaborative recommendation algorithm is devised by combining the reliable neighbor model and robust matrix factorization model.Experimental results show that the proposed algorithm outperforms the existing methods in terms of both recommendation accuracy and robustness. 展开更多
关键词 shilling attacks robust collaborative recommendation matrix factori-zation k-distance Tukey M-estimator
暂未订购 下载PDF
Multi-Domain Collaborative Recommendation with Feature Selection 认领 引用 被引量：3
2
作者 Lizhen Liu Junjun Cui +1 位作者 Wei Song Hanshi Wang 《China Communications》 SCIE CSCD 2017年第8期137-148,共12页
Collaborative f iltering, as one of the most popular techniques, plays an important role in recommendation systems. However,when the user-item rating matrix is sparse,its performance will be degenerate. Recently,domai... Collaborative f iltering, as one of the most popular techniques, plays an important role in recommendation systems. However,when the user-item rating matrix is sparse,its performance will be degenerate. Recently,domain-specific recommendation approaches have been developed to address this problem.The basic idea is to partition the users and items into overlapping domains, and then perform recommendation in each domain independently. Here, a domain means a group of users having similar preference to a group of products. However, these domain-specific methods consisting of two sequential steps ignore the mutual benefi t of domain segmentation and recommendation. Hence, a unified framework is presented to simultaneously realize recommendation and make use of the domain information underlying the rating matrix in this paper. Based on matrix factorization,the proposed model learns both user preferences of multiple domains and preference selection vectors to select relevant features for each group of products. Besides, local context information is utilized from the user-item rating matrix to enhance the new framework.Experimental results on two widely used datasets, e.g., Ciao and Epinions, demonstrate the effectiveness of our proposed model. 展开更多
关键词 collaborative recommendation multi-domain matrix factorization feature selection
暂未订购 下载PDF
Time-Ordered Collaborative Filtering for News Recommendation 认领 引用 被引量：7
3
作者 XIAO Yingyuan AI Pengqiang +2 位作者 Ching-Hsien Hsu WANG Hongya JIAO Xu 《China Communications》 SCIE CSCD 2015年第12期53-62,共10页
Faced with hundreds of thousands of news articles in the news websites,it is difficult for users to find the news articles they are interested in.Therefore,various news recommender systems were built.In the news recom... Faced with hundreds of thousands of news articles in the news websites,it is difficult for users to find the news articles they are interested in.Therefore,various news recommender systems were built.In the news recommendation,these news articles read by a user is typically in the form of a time sequence.However,traditional news recommendation algorithms rarely consider the time sequence characteristic of user browsing behaviors.Therefore,the performance of traditional news recommendation algorithms is not good enough in predicting the next news article which a user will read.To solve this problem,this paper proposes a time-ordered collaborative filtering recommendation algorithm(TOCF),which takes the time sequence characteristic of user behaviors into account.Besides,a new method to compute the similarity among different users,named time-dependent similarity,is proposed.To demonstrate the efficiency of our solution,extensive experiments are conducted along with detailed performance analysis. 展开更多
关键词 similarity collaborative compute recommendation filtering users hundreds Collaborative Recommendation interested
暂未订购 下载PDF
Community-Based User Domain Model Collaborative Recommendation Algorithm 认领 引用 被引量：4
4
作者 Fulan Qian Yanping Zhang +1 位作者 Yuan Zhang Zhen Duan 《Tsinghua Science and Technology》 SCIE EI CAS 2013年第4期353-359,共7页
Collaborative Filtering （CF） is a commonly used technique in recommendation systems. It can promote items of interest to a target user from a large selection of available items. It is divided into two broad classes... Collaborative Filtering （CF） is a commonly used technique in recommendation systems. It can promote items of interest to a target user from a large selection of available items. It is divided into two broad classes： memory-based algorithms and model-based algorithms. The latter requires some time to build a model but recommends online items quickly, while the former is time-consuming but does not require pre-building time. Considering the shortcomings of the two types of algorithms, we propose a novel Community-based User domain Collaborative Recommendation Algorithm （CUCRA）. The idea comes from the fact that recommendations are usually made by users with similar preferences. The first step is to build a user-user social network based on users＇ preference data. The second step is to find communities with similar user preferences using a community detective algorithm. Finally, items are recommended to users by applying collaborative filtering on communities. Because we recommend items to users in communities instead of to an entire social network, the method has perfect online performance. Applying this method to a collaborative tagging system, experimental results show that the recommendation accuracy of CUCRA is relatively good, and the online time-complexity reduces to O.（n）. 展开更多
关键词 collaborative recommendation tagging system community-based recommendation system
暂未订购 下载PDF
Clustering-based recommendation method with enhanced grasshopper optimisation algorithm 认领 引用 被引量：1
5
作者 Zihao Zhao Yingchun Xia +7 位作者 Wenjun Xu Hui Yu Shuai Yang Cheng Chen Xiaohui Yuan Xiaobo Zhou Qingyong Wang Lichuan Gu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第2期494-509,共16页
In the era of big data,personalised recommendation systems are essential for enhancing user engagement and driving business growth.However,traditional recommendation algorithms,such as collaborative filtering,face sig... In the era of big data,personalised recommendation systems are essential for enhancing user engagement and driving business growth.However,traditional recommendation algorithms,such as collaborative filtering,face significant challenges due to data sparsity,algorithm scalability,and the difficulty of adapting to dynamic user preferences.These limitations hinder the ability of systems to provide highly accurate and personalised recommendations.To address these challenges,this paper proposes a clustering-based recommendation method that integrates an enhanced Grasshopper Optimisation Algorithm(GOA),termed LCGOA,to improve the accuracy and efficiency of recommendation systems by optimising cluster centroids in a dynamic environment.By combining the K-means algorithm with the enhanced GOA,which incorporates a Lévy flight mechanism and multi-strategy co-evolution,our method overcomes the centroid sensitivity issue,a key limitation in traditional clustering techniques.Experimental results across multiple datasets show that the proposed LCGOA-based method significantly outperforms conventional recommendation algorithms in terms of recommendation accuracy,offering more relevant content to users and driving greater customer satisfaction and business growth. 展开更多
关键词 collaborative recommendation Grasshopper Optimization Algorithm(GOA) K‐means clustering Lévy flight
暂未订购 下载PDF
Improving Recommendation for Effective Personalization in Context-Aware Data Using Novel Neural Network 认领 引用 被引量：1
6
作者 R.Sujatha T.Abirami 《Computer Systems Science & Engineering》 SCIE EI 2023年第8期1775-1787,共13页
The digital technologies that run based on users’content provide a platform for users to help air their opinions on various aspects of a particular subject or product.The recommendation agents play a crucial role in ... The digital technologies that run based on users’content provide a platform for users to help air their opinions on various aspects of a particular subject or product.The recommendation agents play a crucial role in personalizing the needs of individual users.Therefore,it is essential to improve the user experience.The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites.In Context-Aware Recommender Systems(CARS),several influential and contextual variables are identified to provide an effective recommendation.A substantial trade-off is applied in context to achieve the proper accuracy and coverage required for a collaborative recommendation.The CARS will generate more recommendations utilizing adapting them to a certain contextual situation of users.However,the key issue is how contextual information is used to create good and intelligent recommender systems.This paper proposes an Artificial Neural Network(ANN)to achieve contextual recommendations based on usergenerated reviews.The ability of ANNs to learn events and make decisions based on similar events makes it effective for personalized recommendations in CARS.Thus,the most appropriate contexts in which a user should choose an item or service are achieved.This work converts every label set into a Multi-Label Classification(MLC)problem to enhance recommendations.Experimental results show that the proposed ANN performs better in the Binary Relevance(BR)Instance-Based Classifier,the BR Decision Tree,and the Multi-label SVM for Trip Advisor and LDOS-CoMoDa Dataset.Furthermore,the accuracy of the proposed ANN achieves better results by 1.1%to 6.1%compared to other existing methods. 展开更多
关键词 Recommendation agents context-aware recommender systems collaborative recommendation personalization systems optimized neural network-based contextual recommendation algorithm
暂未订购 下载PDF
Academic Collaborator Recommendation Based on Attributed Network Embedding 认领 引用 被引量：2
7
作者 Ouxia Du Ya Li 《Journal of Data and Information Science》 CSCD 2022年第1期37-56,共20页
Purpose:Based on real-world academic data,this study aims to use network embedding technology to mining academic relationships,and investigate the effectiveness of the proposed embedding model on academic collaborator... Purpose:Based on real-world academic data,this study aims to use network embedding technology to mining academic relationships,and investigate the effectiveness of the proposed embedding model on academic collaborator recommendation tasks.Design/methodology/approach:We propose an academic collaborator recommendation model based on attributed network embedding(ACR-ANE),which can get enhanced scholar embedding and take full advantage of the topological structure of the network and multi-type scholar attributes.The non-local neighbors for scholars are defined to capture strong relationships among scholars.A deep auto-encoder is adopted to encode the academic collaboration network structure and scholar attributes into a low-dimensional representation space.Findings:1.The proposed non-local neighbors can better describe the relationships among scholars in the real world than the first-order neighbors.2.It is important to consider the structure of the academic collaboration network and scholar attributes when recommending collaborators for scholars simultaneously.Research limitations:The designed method works for static networks,without taking account of the network dynamics.Practical implications:The designed model is embedded in academic collaboration network structure and scholarly attributes,which can be used to help scholars recommend potential collaborators.Originality/value:Experiments on two real-world scholarly datasets,Aminer and APS,show that our proposed method performs better than other baselines. 展开更多
关键词 Academic relationships mining Collaborator recommendation Attributed network embedding Deep learning
暂未订购 下载PDF
A Novel Shilling Attack Detection Model Based on Particle Filter and Gravitation 认领 引用 被引量：1
8
作者 Lingtao Qi Haiping Huang +2 位作者 Feng Li Reza Malekian Ruchuan Wang 《China Communications》 SCIE CSCD 2019年第10期112-132,共21页
With the rapid development of e-commerce, the security issues of collaborative filtering recommender systems have been widely investigated. Malicious users can benefit from injecting a great quantities of fake profile... With the rapid development of e-commerce, the security issues of collaborative filtering recommender systems have been widely investigated. Malicious users can benefit from injecting a great quantities of fake profiles into recommender systems to manipulate recommendation results. As one of the most important attack methods in recommender systems, the shilling attack has been paid considerable attention, especially to its model and the way to detect it. Among them, the loose version of Group Shilling Attack Generation Algorithm (GSAGenl) has outstanding performance. It can be immune to some PCC (Pearson Correlation Coefficient)-based detectors due to the nature of anti-Pearson correlation. In order to overcome the vulnerabilities caused by GSAGenl, a gravitation-based detection model (GBDM) is presented, integrated with a sophisticated gravitational detector and a decider. And meanwhile two new basic attributes and a particle filter algorithm are used for tracking prediction. And then, whether an attack occurs can be judged according to the law of universal gravitation in decision-making. The detection performances of GBDM, HHT-SVM, UnRAP, AP-UnRAP Semi-SAD,SVM-TIA and PCA-P are compared and evaluated. And simulation results show the effectiveness and availability of GBDM. 展开更多
关键词 shilling attack detection model collaborative filtering recommender systems gravitation-based detection model particle filter algorithm
暂未订购 下载PDF
Alleviating the Cold Start Problem in Recommender Systems Based on Modularity Maximization Community Detection Algorithm 认领 引用 被引量：4
9
作者 S. Vairachilai M. K. Kavithadevi M. Raja 《Circuits and Systems》 2016年第8期1268-1279,共12页
Recommender system (RS) has become a very important factor in many eCommerce sites. In our daily life, we rely on the recommendation from other persons either by word of mouth, recommendation letters, movie, item and ... Recommender system (RS) has become a very important factor in many eCommerce sites. In our daily life, we rely on the recommendation from other persons either by word of mouth, recommendation letters, movie, item and book reviews printed in newspapers, etc. The typical Recommender Systems are software tools and techniques that provide support to people by identifying interesting products and services in online store. It also provides a recommendation for certain users who search for the recommendations. The most important open challenge in Collaborative filtering recommender system is the cold start problem. If the adequate or sufficient information is not available for a new item or users, the recommender system runs into the cold start problem. To increase the usefulness of collaborative recommender systems, it could be desirable to eliminate the challenge such as cold start problem. Revealing the community structures is crucial to understand and more important with the increasing popularity of online social networks. The community detection is a key issue in social network analysis in which nodes of the communities are tightly connected each other and loosely connected between other communities. Many algorithms like Givan-Newman algorithm, modularity maximization, leading eigenvector, walk trap, etc., are used to detect the communities in the networks. To test the community division is meaningful we define a quality function called modularity. Modularity is that the links within a community are higher than the expected links in those communities. In this paper, we try to give a solution to the cold-start problem based on community detection algorithm that extracts the community from the social networks and identifies the similar users on that network. Hence, within the proposed work several intrinsic details are taken as a rule of thumb to boost the results higher. Moreover, the simulation experiment was taken to solve the cold start problem. 展开更多
关键词 Collaborative Recommender Systems Cold Start Problem Community Detection Pearson Correlation Coefficient
暂未订购 下载PDF
UICF: a new user-item composite filtering recommendation framework by leveraging temporal semantics 认领 引用 被引量：1
10
作者 Qingting Wei Xing Liu +3 位作者 Daming Xian Jianfeng Xu Lan Liu Shiyang Long 《International Journal of Intelligent Computing and Cybernetics》 EI 2024年第3期577-604,共28页
Purpose:The collaborative filtering algorithm is a classical and widely used approach in product recommendation systems.However,the existing algorithms rely mostly on common ratings of items and do not consider tempor... Purpose:The collaborative filtering algorithm is a classical and widely used approach in product recommendation systems.However,the existing algorithms rely mostly on common ratings of items and do not consider temporal information about items or user interests.To solve this problem,this study proposes a new user-item composite filtering(UICF)recommendation framework by leveraging temporal semantics.Design/methodology/approach:The UICF framework fully utilizes the time information of item ratings for measuring the similarity of items and takes into account the short-term and long-term interest decay for computing users’latest interest degrees.For an item to be probably recommended to a user,the interest degrees of the user on all the historically rated items are weighted by their similarities with the item to be recommended and then added up to predict the recommendation degree.Findings:Comprehensive experiments on the MovieLens and KuaiRec datasets for user movie recommendation were conducted to evaluate the performance of the proposed UICF framework.Experimental results show that the UICF outperformed three well-known recommendation algorithms ItemBased Collaborative Filtering(IBCF),User-Based Collaborative Filtering(UBCF)and User-Popularity Composite Filtering(UPCF)in the root mean square error(RMSE),mean absolute error(MAE)and F1 metrics,especially yielding an average decrease of 11.9%in MAE.Originality/value:A UICF recommendation framework is proposed that combines a time-aware item similarity model and a time-wise user interest degree model.It overcomes the limitations of common rating items and utilizes temporal information in item ratings and user interests effectively,resulting in more accurate and personalized recommendations. 展开更多
关键词 Collaborative filtering recommendation Temporal semantics Item similarity User interest
Preference transfer model in collaborative filtering for implicit data 认领 引用 被引量：1
11
作者 Bin JU Yun-tao QIAN Min-chao YE 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2016年第6期489-500,共12页
Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most ... Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users' buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized.Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then,two factor-user matrices can be used to construct a so-called ‘preference dictionary' that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group. 展开更多
关键词 Recommender systems Collaborative filtering Preference transfer model Cross domain Implicit data
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈