219,914 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
Improving the quality of the personalized electronic program guide
As Digital TV subscribers are offered more and more channels, it is becoming increasingly difficult for them to locate the right programme information at the right time. The personalized Electronic Programme Guide (pEPG) is one solution to this problem; it leverages artificial intelligence and user profiling techniques to learn about the viewing preferences of individual users in order to compile personalized viewing guides that fit their individual preferences. Very often the limited availability of profiling information is a key limiting factor in such personalized recommender systems. For example, it is well known that collaborative filtering approaches suffer significantly from the sparsity problem, which exists because the expected item-overlap between profiles is usually very low. In this article we address the sparsity problem in the Digital TV domain. We propose the use of data mining techniques as a way of supplementing meagre ratings-based profile knowledge with additional item-similarity knowledge that can be automatically discovered by mining user profiles. We argue that this new similarity knowledge can significantly enhance the performance of a recommender system in even the sparsest of profile spaces. Moreover, we provide an extensive evaluation of our approach using two large-scale, state-of-the-art online systems—PTVPlus, a personalized TV listings portal and Físchlár, an online digital video library system
Semantic Tagging on Historical Maps
Tags assigned by users to shared content can be ambiguous. As a possible
solution, we propose semantic tagging as a collaborative process in which a
user selects and associates Web resources drawn from a knowledge context. We
applied this general technique in the specific context of online historical
maps and allowed users to annotate and tag them. To study the effects of
semantic tagging on tag production, the types and categories of obtained tags,
and user task load, we conducted an in-lab within-subject experiment with 24
participants who annotated and tagged two distinct maps. We found that the
semantic tagging implementation does not affect these parameters, while
providing tagging relationships to well-defined concept definitions. Compared
to label-based tagging, our technique also gathers positive and negative
tagging relationships. We believe that our findings carry implications for
designers who want to adopt semantic tagging in other contexts and systems on
the Web.Comment: 10 page
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