scholarly journals Building a Personalized College Major Selection Web Page

2018 ◽  
Author(s):  
Ryan L. Boyd ◽  
James W. Pennebaker
Keyword(s):  
Empirical Research ◽  
Recommendation System ◽  
College Major ◽  
Background Knowledge ◽  
Web Page ◽  
Major Selection ◽  
System P ◽  
Computer Analyses ◽  
Extensive List

Nearly 25% of incoming UT-Austin students are unable to get their first two choices for a college major. Historically, these students have been given an extensive list of all potential majors from which to choose. Many students simply lack awareness of the various majors and have no background knowledge that could be helpful in determining whether specific majors would suit their interests or skills.The purpose of this project was to rely on students’ admissions essays to provide a more coherent and tailored set of recommendations when students are selecting an alternative college major. The logic underlying this project is based on decades of empirical research demonstrating that psychological information can be extracted from the language of students’ admissions essays via automated computer analyses. The results of these analyses can then be used to inform the “college major options” webpage so that potential majors most closely aligned with their interests and skills will be displayed first in a recommendation system.

A DYNAMICS RECOMMENDATION SYSTEM BASED ON LOG MINING

2002 ◽  
Vol 13 (04) ◽  
pp. 521-530 ◽  
Author(s):  
WEN GAO ◽  
SHI WANG ◽  
BIN LIU
Keyword(s):  
Recommendation System ◽  
Predictive Accuracy ◽  
Web Pages ◽  
Web Log Mining ◽  
Web Page ◽  
Time Dynamic ◽  
Active User ◽  
Dynamic Web ◽  
Log Mining ◽  
A Current

This paper presents a new real-time, dynamic web page recommendation system based on web-log mining. The visit sequences of previous visitors are used to train a classifier for web page recommendation. The recommendation engine identifies a current active user, and submits its visit sequence as an input to the classifier. The output of the recommendation engine is a set of recommended web pages, whose links are attached to bottom of the requested page. Our experiments show that the proposed approach is effective: the predictive accuracy is quite high (over 90%), and the time for the recommendation is quite small.

scholarly journals Prediction of User Future Request Utilizing the Combination of Both ANN and FCM in Web Page Recommendation

2018 ◽  
Vol 29 (1) ◽  
pp. 583-595 ◽  
Author(s):  
V. Raju ◽  
N. Srinivasan
Keyword(s):  
Clustering Algorithm ◽  
Recommendation System ◽  
Model Data ◽  
Web Page ◽  
Third Phase ◽  
Consumer Access ◽  
User Data ◽  
Fuzzy C Means Clustering ◽  
Neural Network Algorithm ◽  
Projected Process

Abstract This paper explains about the web page recommendation system. This procedure encompasses consumers’ upcoming demand and web page recommendations. In the proposed web page recommendation system, potential and non-potential data can be categorized by use of the Levenberg–Marquardt firefly neural network algorithm, and forecast can be made by using the K-means clustering algorithm. Consequently, the projected representation demonstrates the infrequent contact format with the help of the representation that integrates the comparable consumer access model data that belong to the further consumer. Thereafter, the impending user data are specified to the clustering progression. The third phase of the projected process is collecting potential data with the aid of the improved fuzzy C-means clustering algorithm. The last step of our projected process is envisaging the upcoming demand for the subsequent consumer. The presentation of the projected procedure will be compared to the obtainable procedure.

Web page recommendation system based on partially ordered sequential rules

2017 ◽  
Vol 32 (4) ◽  
pp. 3009-3015 ◽  
Author(s):  
Harpreet Singh ◽  
Manpreet Kaur ◽  
Parminder Kaur
Keyword(s):  
Recommendation System ◽  
Web Page ◽  
Partially Ordered

scholarly journals Webpage Recommendation System Based on the Social Media Semantic Details of the Website

2021 ◽  
Vol 12 (6) ◽  
pp. 237-243
Author(s):  
Dr. R.Rooba Et.al
Keyword(s):  
Recommendation System ◽  
Semantic Information ◽  
Markov Chain Model ◽  
Similarity Score ◽  
Web Pages ◽  
Chain Model ◽  
Web Page ◽  
The Social ◽  
Recommendation Accuracy ◽  
The Web

The web page recommendation is generated by using the navigational history from web server log files. Semantic Variable Length Markov Chain Model (SVLMC) is a web page recommendation system used to generate recommendation by combining a higher order Markov model with rich semantic data. The problem of state space complexity and time complexity in SVLMC was resolved by Semantic Variable Length confidence pruned Markov Chain Model (SVLCPMC) and Support vector machine based SVLCPMC (SSVLCPMC) meth-ods respectively. The recommendation accuracy was further improved by quickest change detection using Kullback-Leibler Divergence method. In this paper, socio semantic information is included with the similarity score which improves the recommendation accuracy. The social information from the social websites such as twitter is considered for web page recommendation. Initially number of web pages is collected and the similari-ty between web pages is computed by comparing their semantic information. The term frequency and inverse document frequency (tf-idf) is used to produce a composite weight, the most important terms in the web pages are extracted. Then the Pointwise Mutual Information (PMI) between the most important terms and the terms in the twitter dataset are calculated. The PMI metric measures the closeness between the twitter terms and the most important terms in the web pages. Then this measure is added with the similarity score matrix to provide the socio semantic search information for recommendation generation. The experimental results show that the pro-posed method has better performance in terms of prediction accuracy, precision, F1 measure, R measure and coverage.

scholarly journals TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources

2020 ◽  
Vol 34 (01) ◽  
pp. 565-573
Author(s):  
Sahan Bulathwela ◽  
Maria Perez-Ortiz ◽  
Emine Yilmaz ◽  
John Shawe-Taylor
Keyword(s):  
Lifelong Learning ◽  
Recommendation System ◽  
Background Knowledge ◽  
Open Educational Resources ◽  
Educational Resources ◽  
Computer Assisted ◽  
Computer Assisted Learning ◽  
Lifelong Learners ◽  
Knowledge States ◽  
Wide Range

The recent advances in computer-assisted learning systems and the availability of open educational resources today promise a pathway to providing cost-efficient high-quality education to large masses of learners. One of the most ambitious use cases of computer-assisted learning is to build a lifelong learning recommendation system. Unlike short-term courses, lifelong learning presents unique challenges, requiring sophisticated recommendation models that account for a wide range of factors such as background knowledge of learners or novelty of the material while effectively maintaining knowledge states of masses of learners for significantly longer periods of time (ideally, a lifetime). This work presents the foundations towards building a dynamic, scalable and transparent recommendation system for education, modelling learner's knowledge from implicit data in the form of engagement with open educational resources. We i) use a text ontology based on Wikipedia to automatically extract knowledge components of educational resources and, ii) propose a set of online Bayesian strategies inspired by the well-known areas of item response theory and knowledge tracing. Our proposal, TrueLearn, focuses on recommendations for which the learner has enough background knowledge (so they are able to understand and learn from the material), and the material has enough novelty that would help the learner improve their knowledge about the subject and keep them engaged. We further construct a large open educational video lectures dataset and test the performance of the proposed algorithms, which show clear promise towards building an effective educational recommendation system.

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