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IEEE Symposium on Computers and Communication (ISCC)

作者： Su, Xin Zhang, Dafang Li, Wenjia Li, Wenwei Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China New York Inst Technol Dept Comp Sci New York NY USA

ISBN: (纸本)9781467371940

A large amount and different types of mobile applications (or apps) are being offered to end users via app markets. These apps normally generate network traffic, which will consumes users' mobile data plan and may even cause potential security issues. However, the amount and type of network traffic generated by a mobile app in the wild is still poorly understood due to the lack of a systematic measurement methodology. In this paper, we first measure and analyze network traffic cost of Android apps in the official Android markets. Based on the results, we find that the apps from different categories have different traffic costs. In particular, there is a remarkable difference among the apps with similar functionality in terms of network traffic cost. Then, we add metrics of traffic cost into our app recommendation algorithm, which differs from the conventional app recommendation approaches. Experimental results show that the proposed recommendation algorithm can effectively help mobile app users avoid various potential security and privacy risks brought by the unnecessary network traffic consumption.

关键词： Android Applications Network Traffic Cost recommendation algorithm

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Information filtering via balanced diffusion on bipartite networks

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2015年 421卷 44-53页

作者： Nie, Da-Cheng An, Ya-Hui Dong, Qiang Fu, Yan Zhou, Tao Univ Elect Sci & Technol China Web Sci Ctr Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Big Data Res Ctr Chengdu 611731 Peoples R China

The recent decade has witnessed the increasing popularity of recommender systems, which help users acquire relevant commodities and services from overwhelming resources on Internet. Some simple physical diffusion processes have been used to design effective recommendation algorithms for user-object bipartite networks, such as mass diffusion (MD) and heat conduction (HC) algorithms, which have different advantages respectively on accuracy and diversity. In this paper, we explore how to combine MD and HC processes to get better recommendation performance and propose a new algorithm mimicking the hybrid of MD and HC processes, named balanced diffusion (BD) algorithm. Numerical experiments on three benchmark data sets, MovieLens, Netflix and RateYourMusic, show that BD algorithm outperforms three typical diffusion-like algorithms on the three important metrics, accuracy, diversity and novelty. Specifically, it not only provides accurate recommendation results, but also yields higher diversity and novelty in recommendations by accurately recommending unpopular objects. (C) 2014 Elsevier B.V. All rights reserved.

关键词： recommendation algorithm Mass diffusion Heat conduction Bipartite network

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Design and Implementation of Recommender System Based on Hadoop

Design and Implementation of Recommender System Based on Had...

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2016 IEEE 7th International Conference on Software Engineering and Service Science

作者： Qing Wang Research Institute of Electronic Science and Technology University of Electronic Science and Technology of China

Recommender system is to establish the relationship between the user and the information products,to use the existing selection habits and the similarity of each user’s potential interest in the object,and then personalized *** is one of the effective ways to solve the information overload in the Internet *** in practical application,because of the large number of products and the number of users,the traditional recommendation system is usually run on the single machine,which has been far from meeting the needs of such big *** this paper,we design and implement a network recommendation algorithm based on Hadoop platform,which is based on the theory of Hadoop and Map/Reduce programming.

关键词： recommender system recommendation algorithm hadoop MapReduce

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Research and implementation of algorithm for short videos recommendation

Research and implementation of algorithm for short videos re...

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IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

作者： Jiang, Lai Fu, Xiangling Beijing Univ Posts & Telecommun BUPT Sch Software Engn Beijing 100088 Peoples R China

ISBN: (纸本)9781479958771

As the approaching of the UGC era, numerous short videos flood into the Internet every day, but at the present stage, it costs much resources and takes a lot of time to calculate the recommended videos. It is inevitable to search for a feasible recommended algorithm. In this paper, this issue has been studied based on distributed computing, innovatively combining the optimization of user tag cloud model. And then we has proposed practical schemes. Experimental results show that the proposed scheme is feasible but also efficient.

关键词： short video UGC recommendation algorithm distributed computing tag cloud user model

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Application For Party Based on Cloud Platform 9

Application For Party Based on Cloud Platform

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Ninth International Conference on Frontier of Computer Science and Technology

作者： Huang, Huan Huang, Ling Wang, Chang-Dong Huang, Jie-Kai Chen, Yan-Jia Sun Yat Sen Univ Sch Mobile Informat Engn Zhuhai 519082 Peoples R China Beijing Inst Technol Sch Accounting & Finance Zhuhai 519088 Peoples R China

ISBN: (纸本)9781467392952

In the past few years, party has played a more and more important role in people's daily life. However, it is not that easy to organize a satisfying party, for the reason that the organizer must consider many details and every member's personal need. In order to help people with this kind of problem, a new and effective tool is needed and we think that an application can be a perfect solution. In this paper, we develop a new Android application, which simplifies the organizing of parties. We also design a specific recommendation algorithm, which helps to offer the organizer some suitable party places, based on people's previous choice, the position of the user, and the evaluations from some review sites. Moreover, using the database deployed on the cloud, our application provides convenience for party members to vote for time or places and for organizers to collect the voting information. The friendly user interface and the use of specific map API make the application more applicable.

关键词： Party organization recommendation algorithm cloud computing database

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Impulse-Response Model for Human Behaviors Sequences 15

Impulse-Response Model for Human Behaviors Sequences

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IEEE 15th International Conference on Data Mining Workshops (ICDMW)

作者： Li, Houyi Han, Banghe Li, Ying Li, Junming Xidian Univ State Key Lab ISN Xian Peoples R China Xidian Univ Sch Math & Stat Xian Peoples R China Xidian Univ Educ Minist China Key Lab IPID Xian Peoples R China

ISBN: (纸本)9781467384933

The interval time distribution is a well investigated in the area of 'human dynamic'. Many research explained the heavy tail phenomenon and reproduced the heavy-tail-like interval time or response time distribution with various models. This paper empirically studies human online activities both at individual level and group level based on T-mall data set and Wikipedia data set. It points out that the statistic features of human behaviors with acquainted objects and unacquainted objects need to be considered independently. Based on research in these two data sets, the timing of human behaviors is a combination of the heavy tail distribution for time interval of executing acquainted objects and the quasi uniform distribution for initial time of executing unacquainted objects. Its shown that this phenomenon is a consequence of inherent causality within human behaviors. This paper proposes Impulse-Response Model to describe this causality. This model connect the two famous problem in human behavior research: the reproduction problem and prediction problem. Time interval distribution of T-mall data set is well reproduced by this model. This paper also show that Impulse-Response Model hold a higher accuracy to make prediction about human future behaviors than traditional classifications both in T-mall data set and Wikipedia data set.

关键词： Behaviors Prediction Heavy Tail Human Dynamics recommendation algorithm

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Rating Prediction using Category Weight Factorization Machine in Bigdata Environment

Rating Prediction using Category Weight Factorization Machin...

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IEEE International Conference on Communications (ICC)

作者： Zhao, Yu Mansouri, Khalil Yang, Yang Mi, Zhenqiang Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China

ISBN: (纸本)9781467363051

Rating Prediction is a key problem in recommendation system, especially in Bigdata environment with data sparsity. Recently, Factorization Machine (FM) has been proven to be effective in solving the recommendation problem. Whereas, valuable category information of users and items are neglected in basic FM model. In this paper, we fully explore the capabilities of category information to improve the accuracy of rating prediction, and proposed a Category Weight Factorization Machine (CW-FM) based on FM. CW-FM utilizes hierarchical category information to avoid the interaction between feature vectors which have the subordinate relations. Combined with user and item category information, CW-FM is proven to be an effective solutions to reducing the rating error in recommendation systems. The proposed CW-FM is evaluated by extensive experiments with real world datasets. Results show that CW-FM model achieves better iterative efficiency and higher rating accuracy compared to contemporary schemes.

关键词： factorization machine rating prediction recommendation algorithm category weights cold start

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Android App recommendation Approach Based on Network Traffic Measurement and Analysis

Android App Recommendation Approach Based on Network Traffic...

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IEEE Symposium on Computers and Communication

作者： Xin Su Dafang Zhang Wenjia Li Wenwei Li College of Computer Science and Electronics Engineering Hunan University Department of Computer Sciences New York Institute of Technology

ISBN: (纸本)9781467371957

关键词： Android Applications Network Traffic Cost recommendation algorithm

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Research on the Collaborative Filtering recommendation algorithm in Ubiquitous computing

Research on the Collaborative Filtering Recommendation Algor...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Wei, Zhi-Qiang Qu, Lian-En Jia, Dong-Ning Zhou, Wei Kang, Mi-Jun Ocean Univ China Coll Informat Sci & Engn Dept Comp Qingdao Shandong Peoples R China Nanchang Hangkong Univ Coll Informat Sci & Engn Dept Comp Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781424467129

It is very difficult for primary users to make up new policies by themselves. To deal with such situation, in this paper a fundamental framework is proposed to fully describe the generation process of policies in pervasive computing applications. Furthermore, the collaborative filtering algorithms based on cosine vector are utilized to calculate characteristic similarity and classic similarity to aggregate the user identity similarity. The machine learning algorithm is adopted to generate the policies which will be recommended to the users. By utilizing the recommended policies, the users can finish the system policies setting process in a more quick and accurate way.

关键词： ubiquitous computing machine learning recommendation system recommendation algorithm collaborative filtering

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A Collaborative Filtering recommendation algorithm Based on Probabilistic Latent Semantic Analysis

A Collaborative Filtering Recommendation Algorithm Based on ...

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2010 International Conference on Management Science and Engineering (MSE 2010)

作者： Guanghua Cheng Zhejiang Business Technology Institute Ningbo 315012,P.R.China

The information overload problem affects everyday experience in the search for valuable *** overcome this problem,people often rely on suggestions from others who have more experience on a *** researchers and practitioners pay more attention on building a proper method which can help users obtain resources and services which *** filtering systems can deal with large numbers of people and with many different *** there is a problem of scalability result in low quality *** this paper,a collaborative filtering recommendation algorithm based on probabilistic latent semantic analysis is *** method uses the probabilistic latent semantic analysis model to predictin at *** then,the presented method utilized the item based collaborative filtering algorithm to produce the *** personalized collaborative filtering approach based on probabilistic latent semantic analysis can alleviate the scalability problem in the collaborative recommendations.

关键词： personalized service collaborative filtering probabilistic latent semantic analysis item similarity recommendation algorithm

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