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检索条件"主题词=recommendation algorithm"

共 370 条记录，以下是71-80 订阅

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Slope One recommendation algorithm Based on User Clustering and Scoring Preferences 3

Slope One Recommendation Algorithm Based on User Clustering ...

引用

3rd International Conference on Mechatronics and Intelligent Robotics (ICMIR)

作者： Song, Yue Ting Wu, Sheng Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China

The Slope One algorithm in the recommendation system has the characteristics of real time efficiency, convenient operation, but at the same time there are not considering between project and the similarity between user's question. In order to improve such problems and improve the accuracy of the algorithm, this paper proposes a Slope One recommendation algorithm that integrates user clustering and scoring preference. First, the similarity among users is measured, and the improved K-means++ algorithm is used to divide users into several categories according to the similarity degree of project preferences. Then, in the category of target users, the Slope One algorithm that integrates users' rating preferences is used to predict the score of projects. And finally top-n recommendation is made according to the predicted score. In this paper, using the Movielens dataset to experiment, the results show that the proposed algorithm can effectively reduce the mean absolute error and root mean square error of traditional algorithm, recommend have higher accuracy and better recommendation quality. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： recommendation algorithm Slope One algorithm Clustering Score preferences

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Research of Personalized recommendation algorithm based on Similarity and Diversity 3

Research of Personalized Recommendation Algorithm based on S...

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3rd IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Shu, Fei Dong, XueTao Chen, ShuTing Qing, Song Hu, MeiHui State Grid Xinjiang Elect Power Res Inst Urumqi Peoples R China State Grid Xinjiang Informat & Commun Corp Urumqi Peoples R China State Grid Xinjiang Elect Power Co Urumqi Peoples R China

ISBN: (纸本)9781538645093

Now the general recommendation system only recommends high similarity products, and if only the similarity is likely to cause the waste of resources and the unsuccessful recommendation, for example, a user likes to use WeChat. Based on the similarity, it is possible to recommend the flying letter to him, so the function of the product is too similar and will cause it. Due to the waste of resources, the difference should be considered properly in the design of recommender systems. The idea of this paper is to complete the research of product attribute similarity algorithm first, and to complete the design of recommendation mechanism based on similarity and difference. Finally, the accuracy and recall rate of various recommendation mechanisms are compared by experiments to determine their superiority.

关键词： recommendation algorithm similarity degree difference degree accuracy

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Research on Personalized recommendation algorithm Based on Mobile Social Network Data 5th

Research on Personalized Recommendation Algorithm Based on M...

引用

5th EAI International Conference on Advanced Hybrid Information Processing (ADHIP)

作者： Yan, Xiang Deng, Wen-hua Puer Univ Informat Ctr Puer 665000 Peoples R China Wuhan Railway Vocat Coll Technol Wuhan 430205 Peoples R China

ISBN: (纸本)9783030945510;9783030945503

Due to the lack of mining of hidden data in traditional personalized recommendation algorithms, the algorithm is interfered by the mobile social network environment, and it is difficult to accurately recommend targeted data for users. Therefore, research on personalized recommendation algorithms based on mobile social network data. By dividing mobile social network user categories, user information is obtained;based on mobile social network data, user demand characteristics are extracted;potential association rules between users and service needs are mined to build personalized recommendation algorithms. The experimental results show that compared with the traditional recommendation algorithm, the research algorithm has stronger perception and recognition ability, and it can recommend more matching information for users according to different user needs when facing different network environments.

关键词： Mobile social network data Personalization recommendation algorithm Association rules

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A Network-Based recommendation algorithm 3

A Network-Based Recommendation Algorithm

引用

3rd International Conference on Computational Intelligence and Applications (ICCIA)

作者： Dai, Xiang Cui, Ying Chen, Zheng Yang, Yi Southwest China Inst Elect Technol ISR Lab Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538695715

As Internet expanding into offline, the traditional retail industry began to use the personalized recommendation algorithm to increase user stickiness, conversion and business income. Without considering the data segmentation problem, traditional recommendation algorithm did not perform well in the traditional business data. Accordingly, we considered the interest spread characteristic of retail industry behavior, adopted the method of complex network to construct a personalized recommendation algorithm using the segmentation data set. By using a real sales dataset of a large supermarket, we provided an evaluation of our algorithm. The results show that our algorithm have much better performance in accuracy and recall than the traditional ones, but with the disadvantage of being less coverage.

关键词： complex network recommendation algorithm traditional retail industry

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Difficulty and Typicality-based Problem recommendation algorithm Implementation and Analysis 8

Difficulty and Typicality-based Problem Recommendation Algor...

引用

8th International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)

作者： Yu, Ruiguo Liu, Zhiqiang Yu, Mei Zhao, Mankun Xu, Tianyi Tianjin Univ Sch Comp Sci & Technol Tianjin 300350 Peoples R China Tianjin Key Lab Cognit Comp & Applicat Tianjin 300350 Peoples R China Tianjin Key Lab Adv Networking Tianjin 300350 Peoples R China

ISBN: (数字)9781538682463

ISBN: (纸本)9781538682463

With the increasing popularity of "online learning", a method of finding effective learning materials from massive resources is urgently needed. By setting the "problems" in the online learning system as an example, this paper analyzes the "difficulty" of learning materials and proposes a computational method of difficulty. Then, based on the difficulty of problems and the concept of "typicality" in psychology, an algorithm suitable for problem recommendation is designed. The comparative experiments based on the actual data prove that this algorithm is superior to the existing methods both in the recommendation effectiveness and the time efficiency.

关键词： recommendation algorithm Collaborative filtering Typicality Online learning Difficulty coefficient

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Personalized Learning Resource recommendation algorithm of Mobile Learning Terminal 15

Personalized Learning Resource Recommendation Algorithm of M...

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15th International Conference on Computational Intelligence and Security (CIS)

作者： Liu, Hui Huang, Kuanna Jia, Liping Leshan Normal Univ Sch Math & Informat Sci Leshen Sichuan Peoples R China

ISBN: (纸本)9781728160924

With the popularization of mobile devices such as the Internet and smart phones, people's learning styles are undergoing profound changes and mobile learning terminals are becoming more and more popular. In this paper, the model of personalized learning resource recommendation algorithm based on mobile learning terminal was studied, and a personalized learning model based on recommendation algorithm was designed. The algorithm can analyze the user's learning history data in the mobile learning terminal and make accurate learning resource recommendation according to the user's learning level and personal preference. Finally, the experimental results show that personalized learning resource recommendation model based on recommendation algorithm can effectively predict the direction of users' learning needs, and expand the new research directions in personalized learning resources recommendation.

关键词： recommendation algorithm personalized learning research

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Personalized recommendation algorithm based on LFM with QoS constraint 6

Personalized recommendation algorithm based on LFM with QoS ...

引用

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 ...

引用

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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Hybrid recommendation algorithm based on logistic regression refinement sorting model 2

Hybrid recommendation algorithm based on logistic regression...

引用

2nd International Conference on Image and Video Processing, and Artificial Intelligence (IPVAI)

作者： Chang, Shihui Weii, Wenguo Xie, Guiyuan Guangdong Polytech Normal Univ Sch Elect & Informat Guangzhou Peoples R China

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

The mainstream recommendation systems mainly use content-based method or collaborative filtering method. However, in specific recommendation scenarios, hybrid algorithm often performs better than single algorithm. We introduce a new recommendation method based on hybrid algorithm, which combined with logistic regression refinement sorting model. Our method can achieve higher accuracy rate and recall rate when we need to consider item and user features comprehensively. We recall and sort items by the hybrid algorithm based on content-based method and collaborative filtering method. After recalling process, we obtain preliminary rough sorting recommendation lists. Then we use logistic regression refinement sorting model to train the rough sorting results. The recommendation results can be more accurate after refinement sorting. We used the song data of a music website as experimental data and set three comparative experiments under different feature weight values. The experimental results show that when we consider the item and user features comprehensively, our method is better than other mainstream methods in accuracy rate and recall rate.

关键词： Logistic Regression Collaborative Filtering Content-Based recommendation algorithm

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Research on Pre-trained Movie recommendation algorithm Based on User Behavior Sequence 12

Research on Pre-trained Movie Recommendation Algorithm Based...

引用

Conference on Optical Design and Testing XII

作者： Zou, Kevin Hou, Xiaohui Li, Tian Xu, Sheng Fuzhou Univ 2 Xueyuan Rd Fuzhou Peoples R China

ISBN: (纸本)9781510656970;9781510656963

In practical application scenarios, the behavior of users watching movies is random and diverse, and also includes spatiotemporal features. Aiming at the fact that the complex ranking model cannot use a large amount of data for learning and updating in real time, especially the problem of insufficient training data for inactive users, this paper proposes a pre-training-based user embedding algorithm model. In the pre-training stage, the SINE model is used to dig out several intents with the highest user interest, improve the hit rate of user interest, and thus improve the accuracy of Inference. The follow-up test results show that the newly constructed recommendation model has better performance, and the evaluation index AUC is increased by 2.4% compared with the model without pre-training, which proves the effectiveness and feasibility of the new algorithm.

关键词： recommendation algorithm Pre-trained User behavior sequence

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