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collaborative filtering algorithms Based on Kendall Correlation in Recommender Systems

Wuhan University Journal of Natural Sciences 2006年第5期11卷 1086-1090页

作者： YAO Yu ZHU Shanfeng CHEN Xinmeng School of Computer Wuhan University Wuhan430072 Hubei China Institute for Chemical Research Kyoto UniversityKyoto 611-0011 Japan

In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users＇ ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage.

关键词： Kendall correlation collaborative filtering algorithms recommender systems positive correlation

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Personal classification space-based collaborative filtering algorithms

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2013年第1期46卷 3-12页

作者： Shirakawa, Takahisa Kurahashi, Setsuya NEC Personal Prod Ltd Shinagawa Ku Tokyo 1410032 Japan Univ Tsukuba Grad Sch Business Sci 3-29-1 Otsuka Bunkyo Tokyo 1120012 Japan

This research proposes a new similar information recommendation system focusing on social bookmarking, which organises bookmarks by using tags. Since social bookmarking targets a wide variety of genres of web pages, this research handles the problem where the standard collaborative filtering method cannot offer recommendations with a better level of precision. This paper improves the collaborative filtering algorithm for users of social bookmark services. A user's bookmarks are placed on his/her own classifying space made of tags. These bookmarks are transformed into a degree of similarity for recommendations. The degree is used to compare the personal classifying space with another's space. Comparison with previous studies confirms the superiority of the method based on space classification, in particular, where the cos distance with the distribution weight added is used as similarity between items. This proposed method shows a significant superiority.

关键词： social bookmarks recommendation system collaborative filtering algorithms multi-genre problem

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Research and Implementation of Personalized Smart Teaching Platform Based on collaborative filtering Algorithm 24

Research and Implementation of Personalized Smart Teaching P...

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Proceedings of the 2024 2nd International Conference on Information Education and Artificial Intelligence

作者： Guoyun Wang Yan Huang Baise University Baise Guangxi China

ISBN: (纸本)9798400711732

Fast expansion of distance education in recent times availed English language learners with a considerable multitude of dynamic resources. But locating the required information from the vast ocean of available resources still remains a significant challenge for the users. An inventive solution to this problem is suggested in the paper making use of an advanced vector space representation along with the k-Means and collaborative filtering techniques for its cluster analysis. The algorithms are able to deftly classify and recommend diverse resources by carefully analyzing user behavior along with individual preferences, thus significantly enhancing the quality of the personalized service. This study concentrates on the possibilities offered by the application of such sophisticated algorithms in English education content retrieval systems, in a bid to improve resource allocation processes and learning pathways, hence achieving both better learning efficiency and user satisfaction.

关键词： collaborative filtering algorithms

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Tourism destination and route planning techniques based on interest distribution matrix and revenue iteration

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GEOJOURNAL 2025年第2期90卷 1-15页

作者： Zhang, Xiaopei Zhengzhou Univ Sci & Technol Coll Business Adm Zhengzhou 450000 Peoples R China

In planning tourism routes, the currently used planning methods cannot plan the optimal routes. Aiming at this problem, the study introduces revenue iteration combined with the improved interest distribution matrix to construct a tourism planning model based on the interest distribution matrix to plan the optimal tourism route. The tourism user interest model is built through the interest distribution matrix, which can effectively collect user interest and preference information and provide various personalized services to users with different requirements. The study adopts the value of the revenue iteration function in revenue iteration to deal with the factors affecting the user's sense of experience. The tourism planning model proposed in the study was compared with two models commonly used for planning tourism routes. Compared to the other two models, the model's mean absolute error value was substantially lower, at just 0.786. Furthermore, the model's mean absolute percentage error consistently stayed below 0.60, suggesting it performed noticeably better than the other two models. Additionally, the tourism planning model indicated in the study was able to plan the best travel routes for various interest preferences, regardless of whether the user requested a destination or not, when compared to morphological spatial pattern analysis and minimum cumulative resistance model. The outcomes reveal that the model can plan the optimal routes that are most suitable for tourism users when planning tourism routes. This model could be implemented in intelligent tourism systems to provide customized, efficient travel planning for diverse user groups.

关键词： Interest distribution matrix Revenue iteration Tourism destinations Route planning collaborative filtering algorithms

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A collaborative filtering recommendation method based on TagIEA expert degree model

A collaborative filtering recommendation method based on Tag...

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International Journal of Computational Science and Engineering

作者： Li, Weimin Wang, Bin Zou, Jianbo Sheng, Jinfang School of Information Science and Engineering Central South University 932 Lushan South Road Changsha Hunan410083 China Department of Information Science and Engineering Hunan University of Humanities Science and Technology Loudi Hunan China

In recent years, social networking services and e-commerce have been developing rapidly. The research of recommending in e-commerce service mainly focused on using the collaborative filtering algorithm. But the algorithm had the limitations of data sparsity and cold start. This paper presents a model using TagIEA expert degree metrics in the context of social e-commerce services, where tag and expert degree information are integrated into the collaborative filtering algorithm. The comprehensive recommendation based on the TagIEA expert degree can effectively mitigate the problems of cold start and data sparsity. Finally, this paper verifies the effectiveness of the improved collaborative filtering algorithm by experiments. Copyright © 2017 Inderscience Enterprises Ltd.

关键词： collaborative filtering Electronic commerce Signal filtering and prediction Social networking (online) Telecommunication services collaborative filtering algorithms collaborative filtering recommendations Data sparsity E commerce services Expert degree Recommendation algorithms SNSs Social networking services

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Evaluation and improvement of personalized recommendation system in low-carbon network marketing

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2024年第19期19卷 2831-2838页

作者： Li, Dan Yang, Xin Xian Univ Technol Sch Econ & Management Xian 710054 Shaanxi Peoples R China Shaanxi Polytech Inst Sch Commercial & Trade Circulat Xianyang 712000 Shaanxi Peoples R China

As an advanced online marketing tool, the personalized recommendation system achieves precise recommendations by analyzing product and user-behavior data, thereby enhancing user experience and satisfaction. This paper proposes a recommendation algorithm based on product attributes and contexts, specifically for low-carbon online marketing. The incorporation of context weights aids the algorithm in more effectively comprehending user needs within specific contexts. Comparative experiments with collaborative filtering and content-based recommendation methods reveal that the hybrid recommendation system proposed in this paper outperforms in terms of recommendation accuracy and user satisfaction, providing theoretical support for low-carbon online marketing.

关键词： low-carbon network marketing personalized recommendations contextual weighting collaborative filtering algorithms

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Research on collaborative filtering Recommendation Algorithm Based on Mahout

Research on Collaborative Filtering Recommendation Algorithm...

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作者： Hui CAO Li-yang YAN Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University

This paper studies the recommended algorithm for the Mahout machine learning platform. The principle analysis of the current mainstream recommendation algorithm is based on project-based collaborative filtering recommendation. The recommendation algorithm of Book-Crossing data set is implemented by using the collaborative filtering algorithm provided by Mahout. The similarity distance and other parameters in the general recommendation algorithm are used to compare and analyze the recommended results.

关键词： Mahout machine learning collaborative filtering algorithms Book recommendation

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The Scoring Matrix Generation Method and Recommendation algorithm in P2P Lending 13

The Scoring Matrix Generation Method and Recommendation algo...

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13th IEEE World Congress on Services (SERVICES)

作者： Zhang, Yanmei Geng, Xiao Jia, Hengyue Cent Univ Finance & Econ Informat Sch Beijing 100081 Peoples R China

ISBN: (纸本)9781538620021

with the rapid development of P2P lending in recent years, how to recommend bidding items to the bidder to meet its preferences will become a research hotspot, but there are few studies in this area. And since there is not a dominant scoring mechanism in P2P lending platform, it is an urgent to solve the problem of how to generate a reasonable score matrix. First of all, Crawling over more than 320 thousand tenders of the last two years from the PPDai. com to. Then, based on the deeply analysis of the data characteristics of the P2P lending areas, three different types of scoring matrix (Boolean, continuous and hybrid) are generated. Then, three kinds of classical collaborative filtering recommendation algorithms are used in the experiment. The results show that different types of scoring matrix has important influence on the performance of the recommendation algorithm, and the performance of cooperative filtering algorithms is different when applying in P2P lending compared with applying in some other fields. Finally, the experimental results are analyzed, and the relevant suggestions are given for the P2P lending.

关键词： P2P lending recommendation collaborative filtering algorithms scoring matrix

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Agricultural Product Rating Prediction Design Based on collaborative filtering Algorithm

Agricultural Product Rating Prediction Design Based on Colla...

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2018 International Symposium on Communication Engineering & Computer Science(CECS 2018)

作者： Bairui Tao Ding Liu Fengjuan Miao Tongri Sun College of Communications and Electronics Engineering Qiqihar University

ISBN: (纸本)9781510873919

The rapid development of agricultural e-commerce has led to more and more agricultural products on the website,and the problem of information overload has become increasingly *** analyzing the user's historical score data set,this paper analyzes the recommended models of different collaborative filtering algorithms and different similarity calculation formulas,predicts the rating of the new agricultural products by the given user,tests the RMSE and MAE values under different algorithms,and helps the users to recommend the preferred agricultural products,thus providing personalized service to the users.

关键词： agricultural e-commerce collaborative filtering algorithms rating prediction RMSE and MAE

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