31,709 research outputs found
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
Towards a learning analytics approach for supporting discovery and reuse of OER: an approach based on Social Networks Analysis and Linked Open Data
The OER movement poses challenges inherent to discovering and reuse digital educational materials from highly heterogeneous and distributed digital repositories. Search engines on today?s Web of documents are based on keyword queries. Search engines don?t provide a sufficiently comprehensive solution to answer a query that permits personalization of open educational materials. To find OER on the Web today, users must first be well informed of which OER repositories potentially contain the data they want and what data model describes these datasets, before using this information to create structured queries. Learning analytics requires not only to retrieve the useful information and knowledge about educational resources, learning processes and relations among learning agents, but also to transform the data gathered in actionable e interoperable information. Linked Data is considered as one of the most effective alternatives for creating global shared information spaces, it has become an interesting approach for discovering and enriching open educational resources data, as well as achieving semantic interoperability and re-use between multiple OER repositories. In this work, an approach based on Semantic Web technologies, the Linked Data guidelines, and Social Network Analysis methods are proposed as a fundamental way to describing, analyzing and visualizing knowledge sharing on OER initiatives
Personalization by Partial Evaluation.
The central contribution of this paper is to model personalization by the programmatic notion of partial evaluation.Partial evaluation is a technique used to automatically specialize programs, given incomplete information about their input.The methodology presented here models a collection of information resources as a program (which abstracts the underlying schema of organization and flow of information),partially evaluates the program with respect to user input,and recreates a personalized site from the specialized program.This enables a customizable methodology called PIPE that supports the automatic specialization of resources,without enumerating the interaction sequences beforehand .Issues relating to the scalability of PIPE,information integration,sessioniz-ling scenarios,and case studies are presented
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