1,339 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
On Recommendation of Learning Objects using Felder-Silverman Learning Style Model
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation
Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners
In this paper, we suggest a novel method to aid lifelong learners to access
relevant OER based learning content to master skills demanded on the labour
market. Our software prototype 1) applies Text Classification and Text Mining
methods on vacancy announcements to decompose jobs into meaningful skills
components, which lifelong learners should target; and 2) creates a hybrid OER
Recommender System to suggest personalized learning content for learners to
progress towards their skill targets. For the first evaluation of this
prototype we focused on two job areas: Data Scientist, and Mechanical Engineer.
We applied our skill extractor approach and provided OER recommendations for
learners targeting these jobs. We conducted in-depth, semi-structured
interviews with 12 subject matter experts to learn how our prototype performs
in terms of its objectives, logic, and contribution to learning. More than 150
recommendations were generated, and 76.9% of these recommendations were treated
as useful by the interviewees. Interviews revealed that a personalized OER
recommender system, based on skills demanded by labour market, has the
potential to improve the learning experience of lifelong learners.Comment: This paper has been accepted to be published in the proceedings of
CSEDU 2020 by SciTePres
Using Semantic Recommenders for Personalized Recommendations
With the ever increasing information overload on the internet, recommender systems have long become a necessity. The popularity of e-commerce sites is increasing by the day and an abundance of shopping sites are presenting users with an increasing number of choices. It has become a challenging task to meet expectations of customers to better understand their needs and provide them with information and suggestions of their interest. With the e-commerce field being fiercely competitive, businesses have started to feel the need of personalization which helps them in building customer loyalty [17]. Personalized recommendations can prove to be the most important aspect of the evolution of the recommender systems. Personalized recommendation services provide opportunities to promote new products, increase sales, click-through and conversion rates [18]. The use of semantic web technologies in recommender systems can effectively enhance the quality of recommendation. Semantic web has provided structured knowledge representation tools such as taxonomies, ontologies, powerful languages such as Resource Description Framework (RDF), Web Ontology Language (OWL), etc. which can be used to represent rich, complex knowledge about things and their relationships and query languages such as SPARQL, reasoning engines that can infer logical consequences from a set of assertions. Semantics enable machines to process natural languages in a manner close to human cognition and mimic human reasoning to a certain extent [12]. This can greatly help to generate personalized predictions in the recommender framework [6]
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