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A new BP algorithm for network marketing performance evaluation of agricultural products

Journal of Applied Sciences 2013年第20期13卷 4332-4335页

作者： Zou, Chun-Fang Huang, Li-Juan School of of Business Administration Jiangxi University of Finance and Economics 330013 Nanchang Jiangxi China School of Information Technology Jiangxi University of Finance and Economics 330013 Nanchang Jiangxi China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics 330013 Nanchang Jiangxi China

Network marketing performance evaluation can sum up and improve the network marketing activities for network enterprises. So, how to evaluate network marketing performance has become a matter of concern in the field related. The study mainly takes agricultural products for example and presents a new algorithm for evaluating network marketing performance based on improved BP neural network algorithm. First, a evaluation indicator system for evaluating network marketing performance of agricultural products is constructed;Second the study analyzes the reasons of the defects of BP neural network algorithm and then designs a new BP algorithm and improves it through changing its learning rate to speed up the calculation and simplify the structure of the original BP algorithm;Finally, the improved algorithm is realized with three e_commerce enterprises are taken for examples to evaluate the network marketing performance and verify the validity and feasibility of the model and the experimental results show that the model can evaluate the network marketing performance of different enterprises practically. © 2013 Asian Network for Scientific Information.

关键词： Agricultural products

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An approach for personalized tag recommendation based on interest transfer model

An approach for personalized tag recommendation based on int...

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9th Web Information Systems and Applications Conference, WISA 2012

作者： Liu, Yue Yang, Nan Yang, Gang School of Information Renmin University of China Bejing China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Bejing China

ISBN: (纸本)9781467330541

Recently, social tagging systems become more and more popular in many Web 2.0 applications. In such systems, Users are allowed to annotate a particular resource with a freely chosen a set of tags. These user-generated tags can represent users' interests more concise and closer to human understanding. Interests will change over time. Thus, how to describe users' interests and interests transfer path become a big challenge for personalized recommendation systems. In this approach, we propose a variable-length time interval division algorithm and user interest model based on time interval. Then, in order to draw users' interests transfer path over a specific time period, we suggest interest transfer model. After that, we apply a classical community partition algorithm in our approach to separate users into communities. Finally, we raise a novel method to measure users' similarities based on interest transfer model and provide personalized tag recommendation according to similar users' interests in their next time intervals. Experimental results demonstrate the higher precision and recall with our approach than classical user-based collaborative filtering methods. © 2012 IEEE.

关键词： Collaborative filtering

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JUFE at INEX 2011 Snippet Retrieval Track

JUFE at INEX 2011 Snippet Retrieval Track

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Lecture Notes in Computer Science

作者： Liu, Dexi Wan, Changxuan Liao, Guoqiong Zhong, Minjuan Liu, Xiping School of Information Technology Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9783642357336

Jiangxi University of Finance and Economics (JUFE) submitted 8 runs to the Snippet Retrieval Track at INEX *** report describes an XML snippet retrieval method based on Average Topic Generalization (ATG) model used by JUFE. The basic idea of the ATG is that different element in an XML document plays different role and hence should has distinguishing importance. The ATG model sets a weight automatically to each element according to its tag or path in the XML document. Then, the BM25EW model based on the ATG is proposed to retrieve and rank the relevant elements in an XML document collection. All windows in the most relevant elements are scored and those windows with higher scores are extracted as snippets. By comparing with the runs under different strategies, the performance are discussed and analyzed in detail. © Springer-Verlag Berlin Heidelberg 2012.

关键词： XML

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FindCredPg: A novel method to find credible pages based on trust web graph

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14th Asia Pacific Web Technology Conference, APWeb 2012

作者： Wang, Teng Zhu, Qing Wang, Shan Liang, Jingfan Key Laboratory of the Ministry of Education for Data Engineering and Knowledge Engineering Renmin University of China Beijing China School of Information Renmin University of China Beijing China

ISBN: (纸本)9783642292521

Finding credible pages is a challenging problem on the Web. Our key observation in this paper is that credible pages usually link to credible content-related pages, which is different from a normal page usually links to normal pages in spam page detection. We propose a novel method to find credible pages based on the trust web graph we define. This method first measures the content correlation between pages connected by hyperlinks, then it combines web link structure with content correlation value of pages to build a trust web graph. At last, credible pages are found successfully by using trust relation of vertices on the trust web graph. We construct a real-world data set by crawling millions of pages on the web and run a set of experiments on this data set. Experiment results show that the accuracy of this method is near 80% and the efficiency is higher. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Hypertext systems

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Summarizing large-scale database schema using community detection

Summarizing large-scale database schema using community dete...

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作者： Wang, Xue Zhou, Xuan Wang, Shan School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China

Schema summarization on large-scale databases is a challenge. In a typical large database schema, a great proportion of the tables are closely connected through a few high degree tables. It is thus dificult to separate these tables into clusters that represent different topics. Moreover, as a schema can be very big, the schema summary needs to be structured into multiple levels, to further improve the usability. In this paper, we introduce a new schema summarization approach utilizing the techniques of community detection in social networks. Our approach contains three steps. First, we use a community detection algorithm to divide a database schema into subject groups, each representing a specific subject. Second, we cluster the subject groups into abstract domains to form a multi-level navigation structure. Third, we discover representative tables in each cluster to label the schema summary. We evaluate our approach on Freebase, a real world large-scale database. The results show that our approach can identify subject groups precisely. The generated abstract schema layers are very helpful for users to explore database. © 2012 Springer Science+Business Media, LLC & Science Press, China.

关键词： database systems

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Efficient SPARQL query processing in mapreduce through data partitioning and indexing

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14th Asia Pacific Web Technology Conference, APWeb 2012

作者： Nie, Zhi Du, Fang Chen, Yueguo Du, Xiaoyong Xu, Linhao Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China IBM Research China Beijing China

ISBN: (纸本)9783642292521

Processing SPARQL queries on single node is obviously not scalable, considering the rapid growth of RDF knowledge bases. This calls for scalable solutions of SPARQL query processing over Web-scale RDF data. There have been attempts for applying SPARQL query processing techniques in MapReduce environments. However, no study has been conducted on finding optimal partitioning and indexing schemes for distributing RDF data in MapReduce. In this paper, we investigate RDF data partitioning technique that provides effective indexing schemes to support efficient SPARQL query processing in MapReduce. Our extensive experiments over a huge real-life RDF dataset show the performance of the proposed partitioning and indexing schemes for efficient SPARQL query processing. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： MapReduce

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Improve top-K recommendation by extending review analysis

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14th Asia Pacific Web Technology Conference, APWeb 2012

作者： Zhu, Qing Xing, Zhe Liang, Jingfan School of Information Renmin University of China Beijing 100872 China Key Laboratory for Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing 100872 China

ISBN: (纸本)9783642292521

The Web has become the popular place for people to purchase product and acquire services, so collaborative filtering is one of the most important algorithms applied in e-commerce recommendation systems. Unfortunately, it is widely recognized that the traditional recommendation methods are inefficient when the user rating data is extremely sparse. In order to overcome the limitations, good recommendation tools are needed to help Web customers determine the products and satisfaction services. In this paper, we propose a multi-dimensional adaptive recommendation algorithm by extending opinion analysis to improve top-k recommendation. In the first step, the novel algorithm that uses extened opinion analysis, creatively combines three dimensional recommendation models: user-based, item-based and opinion-based collaborative filtering. It successfully integrates opinion mining technology with collaborative filtering algorithm. In the second step, we configured the dynamic measurement would help us determine the weight of three dimensions: user-based, item-based and opinion-based analysis, and hence get the final prediction result. The experimental results show that multi-dimensional recommendation can effectively alleviate the dataset sparsity problem and achieve better prediction accuracy compared to other traditional collaborative recommendation algorithms. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Collaborative filtering

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H-tree: A hybrid structure for confidence computation in probabilistic databases

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14th Asia Pacific Web Technology Conference, APWeb 2012

作者： Zhang, Qian Qin, Biao Wang, Shan Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9783642292521

Probabilistic database has become a popular tool for uncertain data management. Most work in the area is focused on efficient query processing and has two main directions, accurate or approximate evaluation. In recent work for conjunctive query without self-joins on a tuple-independent probabilistic database, query evaluation is equivalent to computing marginal probabilities of boolean formulas associated with query results. If formulas can be factorized into a read-once form where every variable appears at most once, confidence computation is reduced to a tractable problem that can be evaluated in linear time. Otherwise, it is regarded as a NP-hard problem and need to be evaluated approximately. In this paper, we propose a framework that evaluates both tractable and NP-hard conjunctive queries efficiently. First, we develop a novel structure H-tree, where boolean formulas are decomposed to small partitions which are either read-once or NP-hard. Then we propose algorithms for building H-tree and parallelizing (approximate) confidence computation. We also propose fundamental theorems to ensure the correctness of our approaches. Performance experiments demonstrate the benefits of H-tree, especially for approximate confidence evaluation on NP-hard queries. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Query processing

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Location selection for utility maximization with capacity constraints 12

Location selection for utility maximization with capacity co...

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21st ACM International Conference on Information and knowledge Management, CIKM 2012

作者： Sun, Yu Huang, Jin Chen, Yueguo Zhang, Rui Du, Xiaoyong School of Information Renmin University of China Beijing China University of Melbourne Melbourne VIC Australia Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE China

ISBN: (纸本)9781450311564

Given a set of client locations, a set of facility locations where each facility has a service capacity, and the assumptions that: (i) a client seeks service from its nearest facility;(ii) a facility provides service to clients in the order of their proximity, we study the problem of selecting all possible locations such that setting up a new facility with a given capacity at these locations will maximize the number of served clients. This problem has wide applications in practice, such as setting up new distribution centers for online sales business and building additional base stations for mobile subscribers. We formulate the problem as location selection query for utility maximization. After applying three pruning rules to a baseline solution,we obtain an efficient algorithm to answer the query. Extensive experiments confirm the efficiency of our proposed algorithm. © 2012 ACM.

关键词： Location

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A typology of online window shopping consumers 16

A typology of online window shopping consumers

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16th Pacific Asia Conference on Information Systems, PACIS 2012

作者： Liu, Fang Wang, Rong Zhang, Ping Zuo, Meiyun School of Information Renmin University of China Beijing China School of Information Studies Syracuse University SyracuseNY United States School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering Beijing China

Consumer online shopping behaviors are well attended in the IS and marketing literature. Yet, there is another group of individuals who spend a lot of time online but do not purchase anything. This online window shopping phenomenon is intriguing to both scholars and marketers yet it is less studied and little understood. Questions such as what the online window shopping consumers do during their visits, how to differentiate their activities and how to design marketing strategies to stimulate them to buy are all essential and beg for investigation. To address this gap, we propose a typology of online window shopping consumers based on the Consumer Information Processing Model, then empirically validate and refine the typology using a set of clickstream data. The final typology contains four main types of online window shopper consumers: 1) promotion finders, 2) social & hedonic experience seekers, 3) information gatherers, and 4) learners & novices. This study extends consumer online behavior research in both e-commerce and social commerce by focusing on the specific group of consumers who only do online window shopping. Besides theoretical contributions, the findings also provide marketers and businesses with valuable references for designing targeted marketing strategies or promotional activities for online window shopping consumers.

关键词： Consumer behavior

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