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2018 4th International Conference on Education,Management and Information Technology(ICEMIT 2018)

作者： Lin Jinjian IBM GBS

Unsupervised algorithms, such as clustering algorithm, could be used on the fault tag position for fault prediction of software module. A software fault prediction algorithm based on quadtree k-means clustering algorithm was proposed in the paper. The purpose of adopting quadtree mainly included two aspects: the first was to seek for clustering center required by k-means clustering algorithm using quadtree, and the second was fault prediction of software module using quadtree. In this algorithm, input threshold parameter decided the initial clustering center. Through changing the threshold parameter, users could get the expected center of clustering. The performance of the algorithm was measured using such a new standard as "clustering earnings". Through simulation and comparison, it was discovered that the algorithm proposed in the paper had highest clustering earnings. Moreover, in most cases, the total error rate of the algorithm proposed in the paper was lower than that of other algorithms, which indicated the effectiveness of the algorithm proposed in the paper in the prediction of software fault.

关键词： k-means clustering recommendation algorithm software prediction diagnosis

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News recommendation System Based on Collaborative Filtering and SVM

News Recommendation System Based on Collaborative Filtering ...

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2018 3rd International Conference on Automation, Mechanical and Electrical Engineering (AMEE 2018)

作者： Wan-li SONG School of Information Engineering Nanjing Xiaozhuang University Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiao Zhuang University

News system requires news classification and personalized recommendation to improve user's efficiency and interest, and to enhance user's experiences. This paper constructed a news automatic classification and recommendation system through natural language processing, text classification, collaborative filtering algorithm. The published news contents were word-segmented and model-trained automatically first to determine which category the news belonging to. Users can also manually modify the classification so that later classification can be updated and improved. After that, the similarity between users was calculated by collaborative filtering and the users having higher similarity with the recommended users were selected. The news seen by the certain users were recommended to the users that were divided into the same group. This paper takes the news corpus of Fudan University's text classification research center as experimental data. Text classification accuracy is tested by this corpus. The experimental results show that the system can serve the news users well. It achieves effective classification and recommendation of news personally.

关键词： recommendation algorithm Automatic classification Similarity Collaborative filtering

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A recommendation Approach based on the Theory of Reasoned Action 12

A Recommendation Approach based on the Theory of Reasoned Ac...

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12th International Conference on Computer Science and Education (ICCSE)

作者： Huang, Xiaoqin Luo, Linkai Peng, Hong Xiamen Univ Dept Automat Xiamen Peoples R China Xiamen Key Lab Big Data Intelligent Anal & Decis Xiamen Peoples R China

ISBN: (纸本)9781509025084

Recommender systems generally provide potentially interesting items for users based on their preferences. In this paper, we propose a method to improve recommendation systems by incorporating the theory of reasoned action. Under the modified model, the behavioral intentions are used to generate recommendations. Compared with user interest in products, the behavioral intentions are more concentrated on user behavior. Hence it is more likely to convert the recommendations of our method into user feedback. The results demonstrate that our

关键词： recommendation algorithm consumer behavior social psychology

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CyVOD: a novel trinity multimedia social network scheme

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第18期76卷 18513-18529页

作者： Zhang, Zhiyong Sun, Ranran Zhao, Changwei Wang, Jian Chang, Carl K. Gupta, Brij B. Henan Univ Sci & Technol Informat Engn Coll Luoyang 471023 Peoples R China Iowa State Univ Dept Comp Sci Ames IA 50011 USA Natl Inst Technol Kurukshetra Haryana India

How to comprehensively explore, improve and deploy multimedia social networks (MSNs) has become a hot topic in the era of emerging pervasive mobile multimedia. More and more MSNs offer a great number of convenient tools, services, and applications for multimedia contents including video and audio that users share willingly and on demand. However, concerns with digital rights management (DRM)-oriented multimedia security, as well as the efficiency of multimedia usage and sharing are meanwhile intensified due to easier distribution and reproduction of multimedia content in a wide range of social networks. The paper proposes a comprehensive framework for multimedia social network, and realized a cross-platform MSN prototype system, named as CyVOD, to support two kinds of DRM modes. The proposed framework effectively protects copyrighted multimedia contents against piracy, and supports a more efficient recommendation system for its better handling of the tradeoff between multimedia security and ease of use.

关键词： Multimedia social network Security Digital rights management recommendation algorithm Prototype

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Research on Passenger Car recommendation based on Comments Mining of Internet 12

Research on Passenger Car recommendation based on Comments M...

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12th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhang, Qinghua Wang, Jian Fan, Kai USTB Sch Mech Engn Beijing Peoples R China

ISBN: (纸本)9781509061617

There are many facts need to be considered when buying a passenger car. The buyer is confused by how to make decision. In this paper, we choose car website recommendation as a decision support. First of all, various car's price, vendor, configuration, ratings and user comments and other information are collected from the car websites, and analyzed by data mining. The user's comments were analyzed to find out the user satisfaction of the various evaluation indicators of the model, extract the advantages and disadvantages of the model. And then according to the comments on the model, we revise the score to improve the accuracy by corresponding the score and comments. According to the different needs of the users, the influencing factors are divided into the basic influencing factors and the influencing factors of the mining results. Based on these factors, we establish a mathematical model, and finally obtain the recommendation function.

关键词： recommendation algorithm comment mining criteria identification car cluster

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A hybrid user similarity model for collaborative filtering

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INFORMATION SCIENCES 2017年 418卷 102-118页

作者： Wang, Yong Deng, Jiangzhou Gao, Jerry Zhang, Pu Chongqing Univ Posts & Telecommun Key Lab Elect Commerce & Logist Chongqing Peoples R China San Jose State Univ Dept Comp Engn San Jose CA 95192 USA

In the neighborhood-based Collaborative Filtering (CF) algorithms, the user similarity has an important effect on the result of CE In order to evaluate the user similarity comprehensively and objectively, we proposed a hybrid model. In the model, an item similarity measure is designed based on the Kullback-Leibler (KL) divergence, which is used as a weight to correct the output of an adjusted Proximity-Significance-Singularity model. Meanwhile, a user preference factor and an asymmetric factor are considered in our model to distinguish the rating preference between difference users and improve the reliability of the model output. The tests on different datasets show that the proposed user similarity model is suitable for the sparse data and effectively improves the prediction accuracy and the recommendation quality. (C) 2017 Elsevier Inc. All rights reserved.

关键词： User similarity Collaborative filtering Common rated items Kullback-Leibler divergence recommendation algorithm

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13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Jiang, Bin Song, Ting-Ting Yang, Chao Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China

ISBN: (纸本)9781538621653

In this paper, we proposed a collaborative filtering recommendation algorithm based on heuristic similarity measure and clustering, in order to alleviate the problem of data sparsity in collaborative filtering algorithm. Firstly, a PSD (Proximity-Significance-Distinction) similarity measure based on rating matrix was proposed, and the score difference in the use of the sigmoid function was made more obvious by expansion of the range of independent variables. On this basis, a multi-factor collaborative filtering recommendation algorithm based on Particle Swarm Optimization (PSO) was proposed, and the PSO algorithm was used to obtain the optimal weight combination of the similarity influence factors, so that the similarity measurement became more accurate. Further, we implemented an improved K nearest neighbor recommendation based on clustering algorithm for generation of a better recommendation list. The method divided the clusters based on the PSD similarity measure proposed in this paper, and searched the nearest K neighbors in the cluster to which the target user belongs, so as to reduce the search time of the nearest neighbor, and obtain a more accurate neighbor set. Finally, a comparative experiment on Movie Lens dataset shows that the proposed algorithm has improved the quality and accuracy of recommendation, thus overcome the data sparseness problem to a certain extent.

关键词： clustering collaborative filtering similarity measure PSO recommendation algorithm

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Design of Educational Big Data Application using Spark 19

Design of Educational Big Data Application using Spark

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19th International Conference on Advanced Communication Technology (ICACT)

作者： Lee, Min-Seon Kim, Earl Nam, Choon-Sung Shin, Dong-Ryeol Sungkyunkwan Univ Dept Informat & Commun Engn 2066 Seobu Ro Suwon Gyeonggi Do South Korea

ISBN: (纸本)9788996865094

Educational institutions utilize various academic systems. Systems related to various tasks such as academic administration, general administration, and subsidiary institution administration are used. These systems provide information with complex and diverse attributes. Students and faculty want to get the information quickly. Also, systems are important for providing services for students and faculty to use their academic information. The academic information has large datasets, among which lecture information can be used to find students' learning patterns. So we use an FP-Growth algorithm that compresses data of frequent items into a frequent pattern tree and then divides the compressed data into a set of related condition data for one frequent item and mining it separately does.

关键词： Spark MLlib recommendation algorithm Big Data

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A Personalized recommendation algorithm on Integration of Item Semantic Similarity and Item Rating Similarity

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JOURNAL OF COMPUTERS 2011年第5期6卷 1047-1054页

作者： Gong, Songjie Zhejiang Business Technol Inst Ningbo 315012 Zhejiang Peoples R China

With the rapid development of the Internet and the wide application of e-commerce, recommender system has become a necessity and collaborative filtering is the most successful technology for building recommendation systems. There are many problems in the recommendation approaches, such as data sparsity problem, the issue of new items and scalability issues. Item-based collaborative filtering algorithms can improve the scalability and the traditional user-based collaborative filtering methods, to avoid the bottlenecks of computing users' correlations by considering the relationships among items. But it still worked poor in solving the issues of sparsity, predictions for new items. In order to effectively solve several problems, this paper presented a recommendation algorithm on integration of item semantic similarity and item rating similarity. The item semantic similarity is calculated combining Earth Mover's Distance and Proportional Transportation Distance, which can utilize the semantic information to measure the similarity between two items based on a solution to the transportation problem from linear optimization1. Then producing recommendation used item-based collaborative filtering integrating the semantic similarity and rating similarity. The presented approach can effectively alleviate the sparsity problem in e-commerce recommender systems.

关键词： recommendation algorithm collaborative filtering semantic similarity rating similarity earth mover's distance proportional transportation distance

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Collaborative Filtering algorithm Based on User Attribute Item Dependency 9

Collaborative Filtering Algorithm Based on User Attribute It...

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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： Xuewang Zhang Xun Jiang Xuewang Zhang School of Software Engineering Chongqing University of Posts and Telecommunication College of Communication Engineering Chongqing University

ISBN: (纸本)9781509038237;9781509038220

Traditional collaborative filtering algorithms, which are mainly based on the users’ rating scores, ignored the effect of user attributes on recommended precision. This paper defines the user attribute item dependency, and proposes a new method for calculating the similarity of user attributes. The algorithm first selects the users who purchased the item, then constructs the virtual user who is the most suitable item from the user’s account of the purchased item, and next uses the virtual user as the center to find the user set which is higher similarity in the item. Finally, the user set which is selected is used as the target user set, and the traditional collaborative filtering algorithm is used to predict user’s score. Our algorithm uses user set who purchased the item as the neighbor screening target set, fill the score matrix with the Slop one *** experimental results show that our algorithm solves the problem that the traditional cooperative filtering does not take into account the user attributes, improves the recommended precision, and solves the sparseness problem. The local performance is improved slightly because that the local user sets are greatly reduced.

关键词： collaborative filtering similarity user attributes recommendation algorithm

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