![]() Packages that you installed will be at depth 0. They'll be listed by depth (the number of nested directories deep). You can view a list of the packages that have been installed by npm using npm list. If you want to globally install Express to use over and over again, use: npm install -g express-generator. ![]() If you try to use the express command or check the version of Express installed using: express -version, you will receive a response that Express cannot be found. We are using the npx command here to execute the Express.js Node package without actually installing it (or by temporarily installing it depending on how you want to think of it). You should see the output: "Hello World". If you need to change the default terminal, select the dropdown menu and choose Select Default Shell. Open your terminal right inside VS Code by selecting View > Terminal (or select Ctrl+`, using the backtick character). Īdd a simple string variable ("Hello World"), then send the contents of the string to your console by entering this in your "app.js" file: var msg = 'Hello World' Open the directory and your app.js file in VS Code using the command: code. Open your command line and create a new directory: mkdir HelloNode, then enter the directory: cd HelloNodeĬreate a JavaScript file named "app.js" with a variable named "msg" inside: echo var msg > app.js If you have not yet installed Visual Studio Code, return to the prerequisite section above and follow the installation steps linked for Windows or WSL. For more information, see Should I install Node.js on Windows or Windows Subsystem for Linux Try NodeJS with Visual Studio Code If you are a beginner, trying Node.js for the first time, we recommend installing directly on Windows. Installing on Node.js on Windows or on Windows Subsystem for Linux.Create your first Node.js web app using Express.Try using Node.js in Visual Studio Code.Abstract: While user-modeling and recommender systems successfully utilize items like emails, news, and movies, they widely neglect mind-maps as a source for user modeling.If you're brand new to using Node.js, this guide will help you to get started with some basics. We consider this a serious shortcoming since we assume user modeling based on mind maps to be equally effective as user modeling based on other items. Hence, millions of mind-mapping users could benefit from user-modeling applications such as recommender systems. The objective of this doctoral thesis is to develop an effective user-modeling approach based on mind maps. To achieve this objective, we integrate a recommender system in our mind-mapping and reference-management software Docear. The recommender system builds user models based on the mind maps, and recommends research papers based on the user models. As part of our research, we identify several variables relating to mind-map-based user modeling, and evaluate the variables' impact on user-modeling effectiveness with an offline evaluation, a user study, and an online evaluation based on 430,893 recommendations displayed to 4,700 users. We find, among others, that the number of analyzed nodes, modification time, visibility of nodes, relations between nodes, and number of children and siblings of a node affect the effectiveness of user modeling. When all variables are combined in a favorable way, this novel approach achieves click-through rates of 7.20%, which is nearly twice as effective as the best baseline. In addition, we show that user modeling based on mind maps performs about as well as user modeling based on other items, namely the research articles users downloaded or cited. Our findings let us to conclude that user modeling based on mind maps is a promising research field, and that developers of mind-mapping applications should integrate recommender systems into their applications. Such systems could create additional value for millions of mind-mapping users.Ībstract: Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery.
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