Based on a careful reading of the case answer the following questions and then complete the table below. Feature Analysis To further analyze the influence of our proposed features on the performance of the question popularity prediction task. Should WNZ be able to edit the work submitted by people who are not employed by the company? Only questions posted as Public are visible on our website. Thus, it indirectly reflects the popularity submitted by the asker. This is because that even the question popularity is highly related with each of the above features, it cannot be effectively modeled by any of the single category features.
Harvard University Tutors. From Figure 3 , to see the green line with error bars, we can observe that the average question popularity sharply increases when the question length varies from 1 to Explain what Zewei’s article from the week 4 reading was all about, assignment help. The results of our approach are in bold. For the consistence of popular question definition with Yahoo!
It also impacted information sharing across the enterprise. Find the average number of moviegoers waiting in line to purchase a ticket, business and finance assignment help. Hence, we first capture the relation between the question submission time and the average question popularity. Instead of directly estimatingwe employ a normalized variant of to derive the estimation:. Throughout this assignment, you need to ask yourself: User Profile the level of the asker who submits the question.
To see the green line with error bars, we can observe that the average question popularity increases consistently with the growing number of answers. Intranet Site An intranet site would benefit the company operationally because it is specifically designed to fit the exact needs of a business. Business Practice Argumentative Essay, business and finance homework help.
These features are deduced from the factors which we have analyzed in the section of Factors Affecting Question Popularity.
SOLUTION: management information system – Studypool
In our experiments, all the prediction approaches and results analysis are performed by using the algorithms implemented in Weka a collection of machine learning algorithms for data mining tasks One can find Weka in http: The sum of the question popularity which obtained by the users equals to 72, Cash Flow Problem Sets, business and finance homework help.
E-commerce site can sell products online Operationally WNZ could benefit from selling products online because it reduces the amount of work that the company has to do to process orders.
Purchase answer to see full attachment. Answers using the Yahoo! In this paper, we investigate the problem of predicting question popularity in CQA.
Table 3 The statistical information of the training and testing data set. Emory University Tutors. Yale University Tutors. The range of question number in subcategories. Inspired by previous work on content quality estimation we utilize question length to measure the question content quality. Introduction Community Question Answering services have emerged as extremely popular alternatives for online information acquisition, such casse Yahoo! And the other features are also weakly correlated with question popularity.
Proceedings of the 19th international conference on World wide web. Selling products online would strategically benefit WNZ because in our modern society the ability to do things quickly over the internet is a major andwers to consumers.
For the evaluation, we use the wzn ofand.
WNZ Case Study
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Proceedings of the 18th interna-tional conference on World wide web. This more personal approach would set WNZ apart from competitors by making their business more personal. It explains how wnz alter business inputs into case that are of greater case than the study cost of case the same outputs. Therefore, we design to explore the relation between question popularity and the number of answers.
To obtain the casee truth of the question popularity prediction task, we normalize the popularity of each question, which is indicated by the number of the user labeled interesting tags, by dividing the maximum popularity value around the whole training and testing data set. This consists of the hardware and system software used to support the applications mentioned previously but not the applications themselves.