Comparison of Generic Similarity Measures in E-learning Content Recommender System in Cold-Start Condition

2020 ◽  
Author(s):  
Jeevamol Joy ◽  
Renumol V G
Keyword(s):  
Recommender System ◽  
Similarity Measures ◽  
Cold Start ◽  
Learning Content ◽  
E Learning

E-Learning Resource Recommendation Based on Fuzzy Sets

2014 ◽  
Vol 513-517 ◽  
pp. 2186-2189
Author(s):  
Lian Hong Ding
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Dynamic Environment ◽  
Similarity Measures ◽  
Learning System ◽  
User Interest ◽  
Learning Materials ◽  
Learning Resource ◽  
Learning Content ◽  
E Learning

A challenging topic for e-learning system is to find appropriate learning assets for users when e-learning takes place in an open and dynamic environment. A good personalized e-learning environment should recommend right learning content for learners. In order to find appropriate learning materials for learners with different preferences, this paper presents a fuzzy set theoretic method for e-learning system. Firstly, e-learning resource and user interest are represented with fuzzy value. Secondly, an algorithm based on various fuzzy set theoretic similarity measures is introduced to find the e-learning contents matching learners request. Lastly, the approach to introduce learning materials for learners, based on the similarity computing, is given. Compared to the baseline crisp set based method presented, our method shows an improvement in precision without loss of recall.

scholarly journals An Item–Item Collaborative Filtering Recommender System Using Trust and Genre to Address the Cold-Start Problem

2019 ◽  
Vol 3 (3) ◽  
pp. 39 ◽  
Author(s):  
Mahamudul Hasan ◽  
Falguni Roy
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measure ◽  
Recommender System ◽  
Similarity Measures ◽  
Cold Start ◽  
Absolute Error ◽  
Prediction Algorithm ◽  
Rating Data ◽  
Asymmetric Correlation ◽  
Cold Start Problem

Item-based collaborative filtering is one of the most popular techniques in the recommender system to retrieve useful items for the users by finding the correlation among the items. Traditional item-based collaborative filtering works well when there exists sufficient rating data but cannot calculate similarity for new items, known as a cold-start problem. Usually, for the lack of rating data, the identification of the similarity among the cold-start items is difficult. As a result, existing techniques fail to predict accurate recommendations for cold-start items which also affects the recommender system’s performance. In this paper, two item-based similarity measures have been designed to overcome this problem by incorporating items’ genre data. An item might be uniform to other items as they might belong to more than one common genre. Thus, one of the similarity measures is defined by determining the degree of direct asymmetric correlation between items by considering their association of common genres. However, the similarity is determined between a couple of items where one of the items could be cold-start and another could be any highly rated item. Thus, the proposed similarity measure is accounted for as asymmetric by taking consideration of the item’s rating data. Another similarity measure is defined as the relative interconnection between items based on transitive inference. In addition, an enhanced prediction algorithm has been proposed so that it can calculate a better prediction for the recommendation. The proposed approach has experimented with two popular datasets that is Movielens and MovieTweets. In addition, it is found that the proposed technique performs better in comparison with the traditional techniques in a collaborative filtering recommender system. The proposed approach improved prediction accuracy for Movielens and MovieTweets approximately in terms of 3.42% & 8.58% mean absolute error, 7.25% & 3.29% precision, 7.20% & 7.55% recall, 8.76% & 5.15% f-measure and 49.3% and 16.49% mean reciprocal rank, respectively.

scholarly journals Clustering Algorithms in Hybrid Recommender System on MovieLens Data

2014 ◽  
Vol 37 (1) ◽  
pp. 125-139 ◽  
Author(s):  
Urszula Kuzelewska
Keyword(s):  
Recommender System ◽  
Web Sites ◽  
Clustering Algorithms ◽  
Leisure Activities ◽  
Similarity Measures ◽  
Clustering Methods ◽  
Log Files ◽  
E Learning ◽  
On Line ◽  
Hybrid Recommender

AbstractDecisions are taken by humans very often during professional as well as leisure activities. It is particularly evident during surfing the Internet: selecting web sites to explore, choosing needed information in search engine results or deciding which product to buy in an on-line store. Recommender systems are electronic applications, the aim of which is to support humans in this decision making process. They are widely used in many applications: adaptive WWW servers, e-learning, music and video preferences, internet stores etc. In on-line solutions, such as e-shops or libraries, the aim of recommendations is to show customers the products which they are probably interested in. As input data the following are taken: shopping basket archives, ratings of the products or servers log files.The article presents a solution of recommender system which helps users to select an interesting product. The system analyses data from other customers' ratings of the products. It uses clustering methods to find similarities among the users and proposed techniques to identify users' profiles. The system was implemented in Apache Mahout environment and tested on a movie database. Selected similarity measures are based on: Euclidean distance, cosine as well as correlation coefficient and loglikehood function.

Promoting Hybrid Learning through an Open Source EBook Approach

2010 ◽  
Vol 14 (3) ◽  
Author(s):  
Xin Bai ◽  
Michael B. Smith
Keyword(s):  
Open Source ◽  
Building Blocks ◽  
Educational Technologies ◽  
Just In Time ◽  
Learning Modules ◽  
Learning Content ◽  
Long Run ◽  
E Learning ◽  
Teaching Efficiency ◽  
Content Design

Educational technology is developing rapidly, making education more accessible, affordable, adaptable, and equitable. Students now have the option to choose a campus that can provide excellent blended learning curriculum with minimal geographical restraints. We proactively explore ways to maximize the power of educational technologies to increase enrollment, reduce failure rates, improve teaching efficiency, and cut costs without sacrificing high quality or placing extra burden on faculty. This mission is accomplished through open source learning content design and development. We developed scalable, shareable, and sustainable e-learning modules as book chapters that can be distributed through both computers and mobile devices. The resulting e-learning building blocks can automate the assessment processes, provide just-in-time feedback, and adjust the teaching material dynamically based upon each student’s strengths and weaknesses. Once built, these self-contained learning modules can be easily maintained, shared, and re-purposed, thus cutting costs in the long run. This will encourage faculty from different disciplines to share their best teaching practices online. The end result of the project is a sustainable knowledge base that can grow over time, benefit all the discipline, and promote learning.

Methodological Problems for Development of Adaptive E-learning Content

2018 ◽  
Vol 9 (1) ◽  
pp. 119-124 ◽  
Author(s):  
Todorka Terzieva ◽  
◽  
Asen Rahnev ◽  
Anatoli Karabov ◽  
◽  
...  
Keyword(s):  
Learning Content ◽  
E Learning ◽  
Methodological Problems

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

2018 ◽  
Vol 2 (4) ◽  
pp. 271 ◽  
Author(s):  
Outmane Bourkoukou ◽  
Essaid El Bachari
Keyword(s):  
Learning Style ◽  
Recommender System ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Research Field ◽  
Learning System ◽  
K Nearest Neighbors ◽  
E Learning ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

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