Web Development with Node.js

This tutorial demonstrates how to teach a Web development course by building web applications with Node.js and Express then deploying to Heroku, a cloud hosting service. This tutorial assumes some familiarity with HTML and JavaScript, but no prior experience with Node.js is necessary. The tutorial covers all necessary setup and step-by-step instructions to build a sample web application with these technologies. The tutorial concludes by describing how to incorporate Node.js, PostgreSQL, Git and Heroku in a web development course, and my experiences with using it for the past two years

WebSiteGen2 : web-based database application generator 2

In this project we implemented WebSiteGen2, which is a software tool that automatically generates HTML pages and server-side scripts for a Web-based database application. A user of WebSiteGen2 can select the tables and columns for which HTML pages and server-side scripts are generated. The menus for this selection process are created from the information stored in the system catalog of a database. Our software tool thus simplifies the implementation of a Web-based database application

Quick Git Setup

Version control is widely adopted in industry because it enables software development in groups, yet few students gain sufficient experience through their undergraduate courses. Even though version control is ideal for work submission, faculty may avoid it in favor of course management systems used only in academia. This tutorial introduces software to automate setting up version control with cloud project hosting services, and gives experience with version control as a side-effect of work submission and collection. This tutorial assumes no prior experience

Collaborative filtering: possibilities for libraries

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Collaborative Filtering for Digital Libraries

Report written in support of a presentation at JCDL 2002.Can collaborative filtering be successfully applied to digital libraries in a manner that improves the effectiveness of the library? Collaborative filtering systems remove the limitation of traditional content-based search interfaces by using individuals to evaluate and recommend information. We introduce an approach where a digital library user specifies their need in the form of a question, and is provided with recommendations of documents based on ratings by other users with similar questions. Using a testbed of the Tsunami Digital Library, we found evidence that suggests that collaborative filtering may decrease the number of search queries while improving users’ overall perception of the system. We discuss the challenges of designing a collaborative filtering system for digital libraries and then present our preliminary experimental results

SERF: integrating human recommendations with search

Today's university library has many digitally accessible resources, both indexes to content and considerable original content. Using off-the-shelf search technology provides a single point of access into library resources, but we have found that such full-text indexing technology is not entirely satisfactory for library searching. In response to this, we report initial usage results from a prototype of an entirely new type of search engine - The System for Electronic Recommendation Filtering (SERF) - that we have designed and deployed for the Oregon State University (OSU) Libraries. SERF encourages users to enter longer and more informative queries, and collects ratings from users as to whether search results meet their information need or not. These ratings are used to make recommendations to later users with similar needs. Over time, SERF learns from the users what documents are valuable for what information needs. In this paper, we focus on understanding whether such recommendations can increase other users' search efficiency and effectiveness in library website searching. Based on examination of three months of usage as an alternative search interface available to all users of the Oregon State University Libraries website (http://osulibrary.oregonstate.edu/), we found strong evidence that the recommendations with human evaluation could increase the efficiency as well as effectiveness of the library website search process. Those users who received recommendations needed to examine fewer results, and recommended documents were rated much higher than documents returned by a traditional search engine

Collaborative filtering for digital libraries

Can collaborative filtering be successfully applied to digital libraries in a manner that improves the effectiveness of the library? Collaborative filtering systems remove the limitation of traditional content-based search interfaces by using individuals to evaluate and recommend information. We introduce an approach where a digital library user specifies their need in the form of a question, and is provided with recommendations of documents based on ratings by other users with similar questions. Using a testbed of the Tsunami Digital Library, we found evidence that suggests that collaborative filtering may decrease the number of search queries while improving users' overall perception of the system. We discuss the challenges of designing a collaborative filtering system for digital libraries and then present our preliminary experimental results.Keywords: user studies, digital libraries, tsunamis, natural hazards, Collaborative filterin

Web Information Retrieval and Filtering Course to Undergraduates Using Open Source Programming

This paper describes how to engage actively students in web information retrieval and filtering course using open source programming. To teach this course, I utilized hands-on lab projects from various open source projects including the Galago search engine. Projects included, but were not limited to, implementing information retrieval (IR) algorithms, collaborative filtering (CF) algorithms, web-based interfaces, and adding features into an open-source search engine. By practicing with real-world open source programming, students found that they better understood how to connect background knowledge to real-world applications in preparation for industry jobs

Collaborative Filtering for Digital Libraries

Can collaborative filtering be successfully applied to digital libraries in a manner that improves the effectiveness of the library? Collaborative filtering systems remove the limitation of traditional content-based search interfaces by using individuals to evaluate and recommend information. We introduce an approach where a digital library user specifies their need in the form of a question, and is provided with recommendations of documents based on ratings by other users with similar questions. Using a testbed of the Tsunami Digital Library, we found evidence that suggests that collaborative filtering may decrease the number of search queries while improving users ’ overall perception of the system. We discuss the challenges of designing a collaborative filtering system for digital libraries and then present our preliminary experimental results

Click data as implicit relevance feedback

Abstract Search sessions consist of a person presenting a query to a search engine, followed by that person examining the search results, selecting some of those search results for further review, possibly following some series of hyperlinks, and perhaps backtracking to previously viewed pages in the session. The series of pages selected for viewing in a search session, sometimes called the click data, is intuitively a source of relevance feedback information to the search engine. We are interested in how that relevance feedback can be used to improve the search results quality for all users, not just the current user. For example, the search engine could learn which documents are frequently visited when certain search queries are given. In this article, we address three issues related to using click data as implicit relevance feedback: (1) How click data beyond the search results page might be more reliable than just the clicks from the search results page; (2) Whether we can further subselect from this click data to get even more reliable relevance feedback; and (3) How the reliability of click data for relevance feedback changes when the goal becomes finding one document for the user that completely meets their information needs (if possible). We refer to these documents as the ones that are strictly relevant to the query. Our conclusions are based on empirical data from a live website with manual assessment of relevance. We found that considering all of the click data in a search session as relevance feedback has the potential to increase both precision and recall of the feedback data. We further found that, when the goal is identifying strictly relevant documents, that it could be useful to focus on last visited documents rather than all documents visited in a search session