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.

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.

scholarly journals PRIVACY PRESERVING CLUSTERING BASED ON LINEAR APPROXIMATION OF FUNCTION

2013 ◽  
Vol 12 (5) ◽  
pp. 3443-3451
Author(s):  
Rajesh Pasupuleti ◽  
Narsimha Gugulothu
Keyword(s):  
Linear Approximation ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Privacy Preserving ◽  
Distance Measures ◽  
Clustering Methods ◽  
Sensitive Data ◽  
Processing Information ◽  
Data Objects ◽  
Approximation Of Function

Clustering analysis initiatives  a new direction in data mining that has major impact in various domains including machine learning, pattern recognition, image processing, information retrieval and bioinformatics. Current clustering techniques address some of the  requirements not adequately and failed in standardizing clustering algorithms to support for all real applications. Many clustering methods mostly depend on user specified parametric methods and initial seeds of clusters are randomly selected by  user.  In this paper, we proposed new clustering method based on linear approximation of function by getting over all idea of behavior knowledge of clustering function, then pick the initial seeds of clusters as the points on linear approximation line and perform clustering operations, unlike grouping data objects into clusters by using distance measures, similarity measures and statistical distributions in traditional clustering methods. We have shown experimental results as clusters based on linear approximation yields good  results in practice with an example of  business data are provided.  It also  explains privacy preserving clusters of sensitive data objects.

Recommender Systems Using Collaborative Tagging

2020 ◽  
Vol 16 (3) ◽  
pp. 183-200
Author(s):  
Latha Banda ◽  
Karan Singh ◽  
Le Hoang Son ◽  
Mohamed Abdel-Basset ◽  
Pham Huy Thong ◽  
...  
Keyword(s):  
Social Networking ◽  
Recommender Systems ◽  
Recommender System ◽  
Prediction Accuracy ◽  
Clustering Algorithms ◽  
Incremental Clustering ◽  
Collaborative Tagging ◽  
Accuracy Ratio ◽  
Hybrid Recommender ◽  
Gradual Decay

Collaborative tagging is a useful and effective way for classifying items with respect to search, sharing information so that users can be tagged via online social networking. This article proposes a novel recommender system for collaborative tagging in which the genre interestingness measure and gradual decay are utilized with diffusion similarity. The comparison has been done on the benchmark recommender system datasets namely MovieLens, Amazon datasets against the existing approaches such as collaborative filtering based on tagging using E-FCM, and E-GK clustering algorithms, hybrid recommender systems based on tagging using GA and collaborative tagging using incremental clustering with trust. The experimental results ensure that the proposed approach achieves maximum prediction accuracy ratio of 9.25% for average of various splits data of 100 users, which is higher than the existing approaches obtained only prediction accuracy of 5.76%.

Ontology Supported Hybrid Recommender System With Threshold Based Nearest Neighbourhood Approach

2019 ◽  
Vol 15 (2) ◽  
pp. 85-107
Author(s):  
Zameer Gulzar ◽  
L. Arun Raj ◽  
A. Anny Leema
Keyword(s):  
Recommender System ◽  
Hybrid Approach ◽  
Learning Systems ◽  
E Learning ◽  
Hybrid Recommender System ◽  
The Individual ◽  
Hybrid Recommender ◽  
Nearest Neighborhood ◽  
Personal Interests ◽  
Hybrid Methodology

Traditional e-learning systems lack the personalization feature to guide learners for selecting the most suitable courses needed. Choosing appropriate courses in the seminal years is important for a future learner who depends on such decisions, as selecting the wrong courses means a mismatch between learner's capability and personal interests. Therefore, a recommender system was developed to suggest and direct the students in selecting the appropriate courses. This study presents algorithms to personalize courses for scholars based on their interests to make learning effective and more productive. The hybrid methodology has been used to retrieve useful information and make accurate recommendations to help learners to increase their performance and improve their satisfaction level. The results suggest that a hybrid approach is better as it will enjoy all the advantages of the individual recommender systems and mitigate their limitations. A threshold-based nearest neighborhood approach will further strengthen the proposed system by finding a similar learner for targeted learners.

The Challenge of Designing User-Centric E-Services

2009 ◽  
pp. 461-478 ◽  
Author(s):  
Patrizia Lombardi ◽  
Ian Cooper ◽  
Krassimira Paskaleva-Shapira ◽  
Mark Deakin
Keyword(s):  
Web Sites ◽  
The European Union ◽  
Research Project ◽  
European Cities ◽  
Knowledge Based ◽  
Knowledge Based Economy ◽  
E Learning ◽  
On Line ◽  
User Centric ◽  
Digital Knowledge

Harnessing ICTs effectively is one of the main vehicles for achieving the EU’s 2010 strategy to become the most competitive digital knowledge-based economy. Achieving this requires innovation and a process of cultural, structural, and economical change towards the so-called eAgora. This requires that citizens are at the center of attention in the design of civic on-line developments in terms of accessibility. This chapter identifies significant challenges to the design of such user-centric e-services, by illustrating some key results of the European Union (EU) IST Framework 6 research project - IntelCities (2004). It presents the City e-governance framework developed in the research project and it shows how the contents of cities’ existing Web sites do not completely satisfy the expectations of the OECD in the European cities visited by the IntelCities Roadshows. It indicates a consistent way forward for the development of the online services offered by the IntelCities e-learning platform. The chapter closes by querying whether either the European cities examined or their citizens have the appetite for the proposed eAgora that will be necessary for its effective implementation and operation.

scholarly journals A Review of Content-Based and Context-Based Recommendation Systems

2021 ◽  
Vol 16 (03) ◽  
pp. 274
Author(s):  
Umair Javed ◽  
Kamran Shaukat ◽  
Ibrahim A. Hameed ◽  
Farhat Iqbal ◽  
Talha Mahboob Alam ◽  
...  
Keyword(s):  
Recommender System ◽  
Recommendation Systems ◽  
Context Aware ◽  
Semantic Approach ◽  
Web Based ◽  
Ontology Language ◽  
E Learning ◽  
Description Framework ◽  
Hybrid Recommender ◽  
Study Context

In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests.

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