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检索条件"主题词=collaborative Filtering Algorithm"

共 111 条记录，以下是21-30 订阅

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An Improved collaborative filtering algorithm to Improve Recommendation Accuracy and Protect User Privacy

An Improved Collaborative Filtering Algorithm to Improve Rec...

引用

IEEE International Conference on Computing, Communications and IoT Applications (IEEE ComComAp)

作者： Yin, Fang Song, Yao Li, Ao Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9781728119731

The emergence of recommendation algorithm alleviates the problem of information overload to a certain extent, makes recommendations to users on the premise of protecting user privacy, and helps users to explore potential demands. An improved collaborative filtering algorithm is proposed for solving the problem that the relationship between recommendation time as well as accuracy cannot be weighed and how to protect user privacy. In this paper, the matrix dimension reduction technique of locally optimized singular value decomposition(SVD) and the K-means clustering technique are used to reduce the dimensions and cluster similar users in the user-item scoring matrix. The approximate difference matrix is used to represent the local structure of the scoring matrix to implement the local optimization. The locally optimized SVD technique can alleviate the problem of data sparsity and poor scalability in collaborative filtering by using fewer iterations. K-means clustering technique can greatly narrow the search range of neighbor sets and improve the recommendation speed. Therefore, collaborative filtering algorithms based on dimensionality reduction and clustering can generate recommendations in an accurate and real-time manner. The experimental results on the MovieLens dataset show that the algorithm can reduce the impact of data sparsity, protect user privacy and effectively improve the accuracy of recommendation.

关键词： collaborative filtering algorithm cluster dimension reduction local optimization user privacy

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A Clustering-Based collaborative filtering Recommendation algorithm via Deep Learning User Side Information 21st

A Clustering-Based Collaborative Filtering Recommendation Al...

引用

21st International Conference on Web Information Systems Engineering (WISE)

作者： Zhao, Chonghao Shi, Xiaoyu Shang, Mingsheng Fang, Yiqiu Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing 400714 Peoples R China Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing Key Lab Big Data & Intelligent Comp Chongqing 400714 Peoples R China

ISBN: (纸本)9783030620080;9783030620073

collaborative filtering (CF) is a widely used recommendation approach that relies on user-item ratings. However, the natural sparsity of user-item ratings can be problematic in many domains, limiting the ability to produce accurate and effective recommendations. Moreover, in some CF approaches only rating information is used to represent users and items, which can lead to a lack of recommendation explained. In this paper, we present a novel deep CF-based recommendation model, which co-learns users' abundant attributes. To better understanding the user's preference, we explore user deeper and unseen factors on the user-item ratings and user's side information by adopting the AutoEncode network. After that, we conduct the k-mean algorithm with extracted deep user factors to classify users. Then the user-side CF algorithm is employed to produce the recommendation list based on the classification results, for alleviating recommendation speed. Finally, we conduct lots of experiments on real-world datasets. Compared with state-of-the-art methods, the results show that the proposed method has a significant improvement in recommendation performance, in terms of recommendation accuracy and diversity. Furthermore, it also enjoys high effectiveness, and the approach is useful when it comes to assigning intuitive meanings to improve the explainability of recommender systems.

关键词： collaborative filtering algorithm Recommendation system K-means plus Clustering

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Clothing Recommendation System Based on Advanced User-Based collaborative filtering algorithm 3rd

Clothing Recommendation System Based on Advanced User-Based ...

引用

3rd Conference on Signal and Information Processing - Networking and Computers (ICSINC)

作者： Liu, Yu Nie, Jingwen Xu, Lexi Chen, Yue Xu, Bingyu Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Queen Mary Univ London London E1 4NS England China Unicom Network Technol Res Inst Beijing 100048 Peoples R China Natl Engn Lab Next Generat Internet Broadband Ser Beijing 100048 Peoples R China

ISBN: (纸本)9789811075216;9789811075209

With the development of e-commerce technology, a growing number of people prefer to purchase clothes on the e-commerce websites. Therefore, an effective recommendation system is necessary for customers. User-based collaborative filtering (UCF) algorithm is widely utilized to predict the preferences of customers. However, UCF algorithm employs the sparse matrix and the recommendation has low precision. In this paper, an improved recommendation algorithm named Advanced User-based collaborative filtering (AUCF) algorithm is proposed and implemented in the clothing recommendation system. The proposed AUCF algorithm introduces user-item linked list, which can overcome the problem of large time complexity. Considering the impact of different popularity of items, AUCF algorithm is capable of publishing the negative influence of popular items, which can increase the recommendation coverage. Experiment results show the AUCF algorithm significantly increases the recommendation coverage and precision.

关键词： collaborative filtering algorithm Online clothing Recommendation system

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An Improved collaborative filtering algorithm to Improve Recommendation Accuracy and Protect User Privacy

An Improved Collaborative Filtering Algorithm to Improve Rec...

引用

IEEE International Conference on Computing, Communications and IoT Applications

作者： Fang Yin Yao Song Ao Li School of Computer Science and Technology Harbin University of Science and Technology

ISBN: (数字)9781728119731

ISBN: (纸本)9781728119748

The emergence of recommendation algorithm alleviates the problem of information overload to a certain extent, makes recommendations to users on the premise of protecting user privacy, and helps users to explore potential demands. An improved collaborative filtering algorithm is proposed for solving the problem that the relationship between recommendation time as well as accuracy cannot be weighed and how to protect user privacy. In this paper, the matrix dimension reduction technique of locally optimized singular value decomposition (SVD) and the K-means clustering technique are used to reduce the dimensions and cluster similar users in the user-item scoring matrix. The approximate difference matrix is used to represent the local structure of the scoring matrix to implement the local optimization. The locally optimized SVD technique can alleviate the problem of data sparsity and poor scalability in collaborative filtering by using fewer iterations. K-means clustering technique can greatly narrow the search range of neighbor sets and improve the recommendation speed. Therefore, collaborative filtering algorithms based on dimensionality reduction and clustering can generate recommendations in an accurate and real-time manner. The experimental results on the MovieLens dataset show that the algorithm can reduce the impact of data sparsity, protect user privacy and effectively improve the accuracy of recommendation.

关键词： collaborative filtering algorithm Cluster Dimension reduction Local optimization User privacy local optimization collaborative filtering Privacy Range searching algorithms Singular value decomposition Consumers difference matrix Suggestion

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A Recommender System for Ordering Platform Based on An Improved collaborative filtering algorithm 6

A Recommender System for Ordering Platform Based on An Impro...

引用

International Conference on Audio, Language and Image Processing (ICALIP)

作者： Yu, Chengchao Tang, Qingshi Liu, Zheng Dong, Bin Wei, Zhihua Tongji Univ Dept Comp Sci & Technol Shanghai 201804 Peoples R China Shanghai Zhuoji Informat Technol Ltd Shanghai 200001 Peoples R China Tongji Univ Key Lab Embedded Syst & Serv Comp Shanghai 201804 Peoples R China

ISBN: (纸本)9781538651957

With the development of ordering platform, an increasing number of people are paying their attention to design a suitable recommender system. Most of the traditional recommender systems are based on the abundant rating information of users. However, Only historical order data can be provided to the recommender system in ordering platform as training data. This paper proposes an improved collaborative filtering algorithm based on historical order data of restaurants. The recommender system includes two parts: 1) rule generation module, we define a new method for measuring the similarity between dishes. Furthermore, we incorporate an incremental learning method in this module. 2) recommendation module, we design user interest vector and propose a noise filtering method. Experimental results demonstrate that the proposed algorithm can effectively improve the performance of recommendation in terms of the accuracy and coverage ratio. Moreover, our recommender system has been successfully put into service.

关键词： recommender system collaborative filtering algorithm incremental learning

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Research on Hotel Recommendation Systems Based on AHP and collaborative filtering Combination algorithm 31

Research on Hotel Recommendation Systems Based on AHP and Co...

引用

31st Chinese Control And Decision Conference (CCDC)

作者： Huang, Chuanlin Cui, Yanqing Sheng, Guojun Dalian Neusoft Univ Informat Sch Informat & Business Managerment Dalian 116023 Peoples R China

ISBN: (纸本)9781728101057

The Internet brings a lot of information to people. While satisfying people's demand for information. it also brings the problem of information overload. Recommendation systems show great potential in solving information overload and personalized needs. This paper studies the recommendation system and hotel recommendation problem for the specific recommendation object of the hotel. By analyzing the current situation and difficult problems of hotel recommendation, this paper proposes a hotel recommendation method combining AMP analytic hierarchy process and collaborative filtering algorithm. This method can quantify the weight of the main factors affecting customer selection, and predict the degree of preference of another customer for a hotel through the rating of similar customer groups, so as to select hotels that are more in line with customer preferences for recommendation.

关键词： Hotel Recommendation Systems Analytic Hierarchy Process collaborative filtering algorithm

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A Brief Analysis of collaborative filtering algorithm Based on Scoring Statistical Prediction

A Brief Analysis of Collaborative Filtering Algorithm Based ...

引用

2018 3rd International Conference on Automation,Mechanical Control and Computational Engineering(AMCCE 2018)

作者： Liu Hongying Xingzhi College of Xi'an University of Finance and Economics

With the development of the Internet and WEB technology over past years, a scoring statistical prediction model can be proposed given the item similarity and the recommended precision, so as to define users and item information. Based on this, a linear regression forecasting model is built and a corresponding algorithm is devised. In view of this, the paper discusses and analyses the collaborative filtering algorithm based on scoring statistical prediction.

关键词： scoring statistics prediction collaborative filtering algorithm

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A collaborative filtering algorithm to Control the Quality Variance in an Environment-Sensitive Process Manufacturing Industry 2

A Collaborative Filtering Algorithm to Control the Quality V...

引用

2nd IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Chai, Zhao Tang, Lin Zhang, Xunan He, Miao Northeastern Univ Comp Sci Boston MA 02115 USA IBM Res China Beijing Peoples R China

ISBN: (纸本)9781509044993

Optimizing the process parameters is recognized as one of the most important steps to reduce the manufacturing variance. In this paper, we proposed a collaborative filtering (CF) algorithm in which the process parameters are optimized referring to the most similar well-controlled historical records in an environment-sensitive process manufacturing. A performance metrics was proposed and numerical studies were conducted to demonstrate the algorithm's validity. The numerical results show that using the Euclidean similarity can achieve a better performance. The findings suggest that the CF algorithm can be effectively applied to not only commerce domain but also manufacturing domain and offer another applicable option to accomplish the precise manufacturing.

关键词： collaborative filtering algorithm quality variance process manufacturing parameters optimization

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An Improved collaborative filtering algorithm Based on RLPSO

An Improved Collaborative Filtering Algorithm Based on RLPSO

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2nd International Conference on Artificial Intelligence and Engineering Applications(AIEA 2017)

作者： YONGLI YANG ZHENHU NING YONGQUAN CAIC FEI XUE SHIQIANG ZHANG HAIFENG LIU Faculty of Information Technology Beijing University of Technology School of Information Beijing Wuzi University Science and Technology on Information Systems Engineering Laboratory Beijing Institute of Control and Electronic Technology

Confront of the large amount of data generated by the Internet and how to make the inherent *** recommendation system is widely used as a means of making effective use of large data and is followed by the *** filtering recommendation algorithm cannot avoid the bottleneck of computing performance problems in the recommendation *** this paper,we propose an improved collaborative filtering recommendation algorithm ***,the RLPSO（Reverse-learning and local-learning PSO）algorithm is used to find the optimal solution of particle swarm and output the optimized clustering ***,the RLPSOM algorithm is used to cluster the user ***,the traditional collaborative filtering algorithm is combined with RLPSOM clustering to effectively recommend the target *** experimental results show that the RLPSOMF algorithm has a significant improvement in the recommended accuracy and has a higher stability.

关键词： collaborative filtering algorithm RLPSO algorithm K-means algorithm

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Research on collaborative filtering algorithm Based on MapReduce 9

Research on Collaborative Filtering Algorithm Based on MapRe...

引用

9th International Symposium on Computational Intelligence and Design (ISCID)

作者： Cai, Rui Li, Chen Beijing Informat Sci & Technol Univ Beijing Peoples R China

ISBN: (纸本)9781509035588

In the wake of growing explosion of data and variable data format, collaborative filtering algorithm as a technology of E-commerce recommendation system cannot satisfy the requirement. Taking this as a starting point, this paper proposes a collaborative filtering algorithm based on MapReduce by using the MapReduce technology to realize the parallel filtering algorithm. In this algorithm, the data is processed by inverted index technique and soft matching strategy, which is used to draw lessons from the co word analysis in order to optimize the similarity computation and algorithm. Finally, the collaborative filtering algorithm in the MapReduce framework has been implemented.

关键词： Electronic Commerce Recommendation system MapReduce collaborative filtering algorithm

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