33,149 research outputs found
Web Site Personalization based on Link Analysis and Navigational Patterns
The continuous growth in the size and use of the World Wide Web imposes new methods of design and development of on-line information services. The need for predicting the users’ needs in order to improve the usability and user retention of a web site is more than evident and can be addressed by personalizing it. Recommendation algorithms aim at proposing “next” pages to users based on their current visit and the past users’ navigational patterns. In the vast majority of related algorithms, however, only the usage data are used to produce recommendations, disregarding the structural properties of the web graph. Thus important – in terms of PageRank authority score – pages may be underrated. In this work we present UPR, a PageRank-style algorithm which combines usage data and link analysis techniques for assigning probabilities to the web pages based on their importance in the web site’s navigational graph. We propose the application of a localized version of UPR (l-UPR) to personalized navigational sub-graphs for online web page ranking and recommendation. Moreover, we propose a hybrid probabilistic predictive model based on Markov models and link analysis for assigning prior probabilities in a hybrid probabilistic model. We prove, through experimentation, that this approach results in more objective and representative predictions than the ones produced from the pure usage-based approaches
Visual analytics in FCA-based clustering
Visual analytics is a subdomain of data analysis which combines both human
and machine analytical abilities and is applied mostly in decision-making and
data mining tasks. Triclustering, based on Formal Concept Analysis (FCA), was
developed to detect groups of objects with similar properties under similar
conditions. It is used in Social Network Analysis (SNA) and is a basis for
certain types of recommender systems. The problem of triclustering algorithms
is that they do not always produce meaningful clusters. This article describes
a specific triclustering algorithm and a prototype of a visual analytics
platform for working with obtained clusters. This tool is designed as a testing
frameworkis and is intended to help an analyst to grasp the results of
triclustering and recommender algorithms, and to make decisions on
meaningfulness of certain triclusters and recommendations.Comment: 11 pages, 3 figures, 2 algorithms, 3rd International Conference on
Analysis of Images, Social Networks and Texts (AIST'2014). in Supplementary
Proceedings of the 3rd International Conference on Analysis of Images, Social
Networks and Texts (AIST 2014), Vol. 1197, CEUR-WS.org, 201
RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests
Various forms of Peer-Learning Environments are increasingly being used in
post-secondary education, often to help build repositories of student generated
learning objects. However, large classes can result in an extensive repository,
which can make it more challenging for students to search for suitable objects
that both reflect their interests and address their knowledge gaps. Recommender
Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution
to this problem by providing sophisticated filtering techniques to help
students to find the resources that they need in a timely manner. Here, a new
RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is
presented. The approach uses a collaborative filtering algorithm based upon
matrix factorization to create personalized recommendations for individual
students that address their interests and their current knowledge gaps. The
approach is validated using both synthetic and real data sets. The results are
promising, indicating RiPLE is able to provide sensible personalized
recommendations for both regular and cold-start users under reasonable
assumptions about parameters and user behavior.Comment: 25 pages, 7 figures. The paper is accepted for publication in the
Journal of Educational Data Minin
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