2,161 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
An architecture for life-long user modelling
In this paper, we propose a united architecture for the creation of life-long user profiles. Our architecture combines different steps required for a user prole, including feature extraction and representation, reasoning, recommendation and presentation. We discuss various issues that arise in the context of life-long profiling
Social semantic search : a case study on web 2.0 for science
When researchers formulate search queries to find relevant content on the Web, those queries typically consist of keywords that can only be matched in the content or its metadata. The Web of Data extends this functionality by bringing structure and giving well-defined meaning to the content and it enables humans and machines to work together using controlled vocabularies. Due the high degree of mismatches between the structure of the content and the vocabularies in different sources, searching over multiple heterogeneous repositories of structured data is considered challenging. Therefore, the authors present a semantic search engine for researchers facilitating search in research related Linked Data. To facilitate high-precision interactive search, they annotated and interlinked structured research data with ontologies from various repositories in an effective semantic model. Furthermore, the authors' system is adaptive as researchers can synchronize using new social media accounts and efficiently explore new datasets
An MPEG-7 scheme for semantic content modelling and filtering of digital video
Abstract Part 5 of the MPEG-7 standard specifies Multimedia Description Schemes (MDS); that is, the format multimedia content models should conform to in order to ensure interoperability across multiple platforms and applications. However, the standard does not specify how the content or the associated model may be filtered. This paper proposes an MPEG-7 scheme which can be deployed for digital video content modelling and filtering. The proposed scheme, COSMOS-7, produces rich and multi-faceted semantic content models and supports a content-based filtering approach that only analyses content relating directly to the preferred content requirements of the user. We present details of the scheme, front-end systems used for content modelling and filtering and experiences with a number of users
Personalization of Queries based on User Preferences
Query Personalization is the process of dynamically enhancing a query with related user preferences stored in a user profile with the aim of providing personalized answers. The underlying idea is that different users may find different things relevant to a search due to different preferences. Essential ingredients of query personalization are: (a) a model for representing and storing preferences in user profiles, and (b) algorithms for the generation of personalized answers using stored preferences. Modeling the plethora of preference types is a challenge. In this paper, we present a preference model that combines expressivity and concision. In addition, we provide algorithms for the selection of preferences related to a query and the progressive generation of personalized results, which are ranked based on user interest
concept paper
In this concept paper, we outline our working plan for the next phase of the
Corporate Semantic Web project. The plan covers the period from March 2009 to
March 2010. Corporate ontology engineering will improve the facilitation of
agile ontology engineering to lessen the costs of ontology development and,
especially, maintenance. Corporate semantic collaboration focuses the human-
centered aspects of knowledge management in corporate contexts. Corporate
semantic search is settled on the highest application level of the three
research areas and at that point it is a representative for applications
working on and with the appropriately represented and delivered background
knowledge. Each of these pillars will yield innovative methods and tools
during the project runtime until 2013. We propose a concept draft and a
working plan covering the next twelve months for an integrative architecture
of a Corporate Semantic Web provided by these three core pillars
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