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Conference on the Future Computer and Control Systems

作者： Huang, Yibo Zhejiang Text & Fash Coll Ningbo 315211 Zhejiang Peoples R China

ISBN: (纸本)9783642293863

recommendation system is becoming an important part of electronic commerce systems. And collaborative filtering is the most hot research topic for building electronic commerce personalized recommendation system and is extensively used in many fields. Collaborative filtering aims at predicting a target user's ratings for new items by integrating other like-minded users rating information. The user-based approach is a common technique used in collaborative filtering. This method first uses statistical approaches to measure user similarities based on their previous ratings on different items. Users will then be grouped into different neighborhood according to the calculated similarities. Finally, the approach will generate predictions on how a user would rate a specific item by aggregating ratings on the item cast by the identified neighbors of the target user. Collaborative filtering algorithm usually suffers from two fundamental problems: sparsity and scalability. In this paper, the problems of sparsity and scalability are described. And an overview of collaborative filtering recommendation algorithm in electronic commerce is presented.

关键词： collaborative filtering electronic commerce recommendation algorithm sparsity scalability

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The Comparison of Efficiency between the recommendation algorithm Based on Multi-Attribute Rating Matrix and the algorithm Based on UIARM

The Comparison of Efficiency between the Recommendation Algo...

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International Conference on Advanced Materials and Engineering Materials (ICAMEM 2011)

作者： Yin Hang Chang Guiran Wang Xingwei Northeastern Univ Sch Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9783037853559

this recommendation algorithm based on User-Item Attribute Rating Matrix (UIARM) can solve the cold-start problem, but the recommended low efficiency, poor quality. The use of Multi-Attribute Rating Matrix (MARM) can solve this problem;it can reduce the computation time and improve the recommendation quality effectively. The user information is analyzed to create their attribute-tables. The user's ratings are mapped to the relevant item attributes and the user's attributes respectively to generate a User Attribute-Item Attribute Rating Matrix. After UAIARM is simplified, MARM will be created. When a new item/user enters into this system, the attributes of new item/user and MARM are matched to find the N users/item with the highest match degrees as the target of the new items or the recommended items. Experiment results validate the cold-start recommendation algorithm based on MARM is efficient.

关键词： recommendation algorithm rating matrix cold-start attribute-tables

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Collaborative recommendations using Hierarchical Clustering based on K-d Trees and Quadtrees

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INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2019年第4期27卷 637-668页

作者： Das, Joydeep Majumder, Subhashis Gupta, Prosenjit Mali, Kalyani Heritage Acad Kolkata W Bengal India Heritage Inst Technol Dept Comp Sci & Engn Kolkata W Bengal India NIIT Univ Dept Comp Sci Neemrana Rajasthan India Univ Kalyani Dept Comp Sci & Engn Kalyani W Bengal India

Majority of the e-commerce sites implement Recommender Systems (RS) to help users navigate through the large search space and assist their decision making process by suggesting products that the user may like. Collaborative Filtering (CF) is the most successful and widely used algorithm in the domain of RS. However, due to the exponential growth of the web in terms of both content and number of users, CF based RS face serious scalability issues. To alleviate this problem, we propose a clustering based CF approach using two hierarchical space partitioning data structures - K-d tree and Quadtree. We cluster or partition the users' space of the system on the basis of user location and then use the resultant clusters for predicting ratings of a target user. Since the CF based recommendation algorithm is applied separately to the clusters and not on the entire rating data, it helps in bringing down the runtime of the algorithm substantially. We further measure spatial autocorrelation indices in the clusters to justify our clustering method. However, our objective is not only to reduce the runtime but also to maintain an acceptable recommendation quality. This requirement is rightly addressed by the proposed method which assures scalability, by processing very large datasets using the same computing resource. Moreover our proposed clustering scheme is oblivious of the underlying CF algorithm. Results from the extensive experiments conducted, show that our hierarchical clustering based recommendation approach reduces runtime of the standard CF algorithms by about 88%, 82%, 79% and 85% for MovieLens-100K, MovieLens-1M, Book-Crossing and TripAdvisor data respectively, while maintaining good recommendation quality.

关键词： Collaborative filtering K-d trees quadtrees recommendation algorithm spatial autocorrelation scalability

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The New algorithm of Commodity recommendation Based on MapReduce

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AGRO FOOD INDUSTRY HI-TECH 2017年第3期28卷 3188-3191页

作者： Wei, Zhao Wei, Liu Zhengzhou Univ Software & Appl Sci & Technol Inst Zhengzhou Henan Peoples R China

In view of the low efficiency of traditional collaborative filtering algorithm in personalized recommendation against the backdrop of mass data era, this paper, based on MapReduce parallel programming model, proposes a modified parallelization of ItemCF algorithm, which parallelizes the traditional ItemCF on Hadoop platform. This paper also elaborates the steps and details of the parallelization. This algorithm solves the problem of the operation of the ItemCF algorithm in large-scale data. The result of the experiment shows that the improved ItemCF algorithm has obviously better performance and speedup in personalized recommendation for commodity than the one realized on single node.

关键词： recommendation algorithm parallel computing similarity collaborative filtering

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Research and Implementation on User Interest Modeling based on Genetic algorithm 3

Research and Implementation on User Interest Modeling based ...

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

作者： Shu, Fei Li, Ming Chen, ShuTing Chen, Jia Dong, XueTao State Grid Xinjiang Elect Power Res Inst Urumqi Peoples R China State Grid Xinjiang Informat & Commun Corp Urumqi Peoples R China

ISBN: (纸本)9781538645093

Interest model of user is the basic part of personalized recommendation. It represents the interests of users and is the basic guarantee for successful recommendation. The quality of user model is directly related to the recommendation precision of personalized recommendation system. The user model is the main source of personalized recommendation system for personalized recommendation. The ability of user interest model to reflect the user's real preference is the key to the personalized recommendation system. In various industries, the establishment of user models has a positive effect on operators' successful recommendation of products to enhance competitiveness. The combination of user interest modeling based on genetic algorithm and the combination of tree-based product models shows good performance and recommendation effect. Finally, it is proved that the recommendation mechanism based on the behavior and function similarity based on the difference of content is better than the recommendation algorithm based on product similarity and other recommendation mechanisms that add product differentiation.

关键词： interest model genetic algorithm recommendation algorithm tree-based

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Research on Personalized Exercises and Teaching Feedback Based on Big Data 18

Research on Personalized Exercises and Teaching Feedback Bas...

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3rd International Conference on Intelligent Information Processing (ICIIP)

作者： Xie, Xiaolan Li, Xinrong Guilin Univ Technol Coll Informat Sci & Engn Guilin Guangxi Peoples R China Guilin Univ Technol Guangxi Key Lab Embedded Technol & Intelligent Sy Guilin Guangxi Peoples R China

ISBN: (纸本)9781450364966

With the rapid development of information technology, big data plays an increasingly important role in the research and practice of education and teaching. Online education has also become a research hotspot. To solve the problem of lack of personalized exercises and accurate teaching feedback in online education, a content-based recommendation model in big data and a clustering model based on EM algorithm is proposed in this paper. First of all, the students' answer of questions is recorded. Then the characteristic information is extracted, so recommends of the exercises are provided by the model according to the personal characteristic information. Then, all the students' recommendation information is stored in the feature library, in which the information of students are clustered, and the teaching effect is fed back according to the characteristic parameters of each category. On the one hand, the status of students' learning is fed back;On the other hand, the level of teachers' teaching level is also fed back. Finally, the model works well through experiments, with the good performance that it can improve the efficiency of online learning.

关键词： online learning recommendation algorithm EM algorithm personalized exercises teaching feedback

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Social recommendation based on trust and influence in SNS environments

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第9期76卷 11585-11602页

作者： Li, Weimin Ye, Zhengbo Xin, Minjun Jin, Qun Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China China Jiliang Univ Coll Informat Engn Hangzhou Zhejiang Peoples R China Waseda Univ Fac Human Sci Tokorozawa Saitama Japan

The development of social media provides convenience to people's lives. People's social relationship and influence on each other is an important factor in a variety of social activities. It is obviously important for the recommendation, while social relationship and user influence are rarely taken into account in traditional recommendation algorithms. In this paper, we propose a new approach to personalized recommendation on social media in order to make use of such a kind of information, and introduce and define a set of new measures to evaluate trust and influence based on users' social relationship and rating information. We develop a social recommendation algorithm based on modeling of users' social trust and influence combined with collaborative filtering. The optimal linear relation between them will be reached by the proposed method, because the importance of users' social trust and influence varies with the data. Our experimental results show that the proposed algorithm outperforms traditional recommendation in terms of recommendation accuracy and stability.

关键词： recommendation algorithm Similarity Trust Social influence

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Attribute Weighting and Samples Sampling for Collaborative Filtering

Attribute Weighting and Samples Sampling for Collaborative F...

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IEEE International Conference on Big Data and Smart Computing (BigComp)

作者： Qu, Zhaowei Yao, Jingjing Wang, Xiaoru Yin, Sixing Beijing Univ Posts & Telecommun Inst Network Technol Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Inst Comp Sci Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Inst Informat & Commun Engn Beijing 100876 Peoples R China

ISBN: (纸本)9781538636497

Item-Based Collaborative filtering is used in many fields. However, it recommends movies for user only according to the user's behavior records. Therefore, it fails to recommend effectively and solve problems such as cold start. To solve these problems, this paper proposes an attribute weighting collaborative filtering (AW - CF). The algorithm not only takes advantage of the movies' rating information from users but also the movies' attribute information to measure the similarity between movies. We can calculate the similarity between movies more accurate by using the movies' attribute information. Besides, we proposed an method of sampling the samples to represent the train dataset so that we can reduce training time (sampling AW-CF). The experiments based on the dataset of MovieLens show that the proposed AW-CF and sampling AW-CF algorithms achieved good effect in MAE, RMSE and make more efficient recommendations. Their effect are superior to the compared algorithms.

关键词： Collaborative filtering Back Propagation Neural Networks Cold start Scalability recommendation algorithm

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A collaborative filtering recommendation method based on TagIEA expert degree model

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2017年第4期14卷 321-329页

作者： Li, Weimin Wang, Bin Zou, Jianbo Sheng, Jinfang Cent S Univ Sch Informat Sci & Engn 932 Lushan South Rd Changsha 410083 Hunan Peoples R China Hunan Univ Humanities Sci & Technol Dept Informat Sci & Engn Loudi Hunan Peoples R 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.

关键词： collaborative filtering tag expert degree social networking services SNSs recommendation algorithm

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Library Book recommendation System of Tianjin University of Technology

Library Book Recommendation System of Tianjin University of ...

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The 2018 International Symposium on Humanities and Social Sciences,Management and Education Engineering(HSSMEE 2018)

作者： Zishuai Yang Yingchun Wang Yanquan Liu School of Tianjin Technology University School of America Southern Connecticut State University(New Haven)

ISBN: (纸本)9781510898837

This paper makes a systematic and in-depth study on the functional module design of the book recommendation system in the library of Tianjin University of Technology. In order to ensure the standardization and scientificalness of the research and development process and meet the needs of library use, the research and field investigation methods are comprehensively used, and the recommendation system theory and methods are used for reference to understand the relevant recommendation algorithms and strictly standardize the criteria for consideration thus completing the design of the functional module of the book recommendation system in the library of Tianjin University of Technology.

关键词： Library Personalized recommendation recommendation algorithm Efficient Libraries Recommender systems Tientsin function block

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