Ontology-Based Matchmaking Approach for Context-Aware Recommendations

Naudet, Yannick; Mignon, Sabrina; Lecaque, L.; Hazotte, C.; Grouès, Valentin

doi:10.1109/axmedis.2008.38

Cited by 8 publications

(2 citation statements)

References 4 publications

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“…In [22], Qin Wijun et al presented an ontology based context middleware for smart spaces, allowing unambiguous definition of context concepts and enriching them in cases where the context data is incomplete or inaccurate due to sensing error. In [23] and [24], contextual information was represented using ontologies. In [25], Barbosa at al present a multimedia context aware ontology (MULTICAO) combined with MPEG-21 for content adaptation decision.…”

Section: Previous Workmentioning

confidence: 99%

Context Representation for Context-Aware Mobile Multimedia Content Recommendation

Otebolaku¹,

Andrade²

2011

Internet and Multimedia Systems and Applications / 747: Human-Computer Interaction

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Very few of the current solutions for content recommendation take into consideration the context of usage when analyzing the preferences of the user and issuing recommendations. Nonetheless, context can be extremely useful to help identify appropriate content for the specific situation or activity the user is in, while consuming the content. In this paper, we present a solution to allow content-based recommendation systems to take full potential of contextual data, by defining a standards-based representation model which accounts for possible relationships among low-level contexts. The MPEG-7 and MPEG-21 standards are used for content description and low-level context representation. OWL/RDF ontologies are used to capture contextual concepts and, together with SWRL to establish relationships and perform reasoning to derive high-level concepts the way humans do. This knowledge is then used to drive the recommendation and content adaptation processes. As a side achievement, an extension to the MPEG-21 specification was developed to accommodate the description of user activities, which we believe have a great impact on the type of content to be recommended.

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Section: Previous Workmentioning

confidence: 99%

Context Representation for Context-Aware Mobile Multimedia Content Recommendation

Otebolaku¹,

Andrade²

2011

Internet and Multimedia Systems and Applications / 747: Human-Computer Interaction

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“…The typical approach used by filtering systems consists on a hybrid recommender system, i.e., one that uses both content-based and collaborative filtering, minimizing the problems that these approaches have individually. Additionally, in order to create new recommendation models or to improve recommendation, new approaches have been proposed, such as the use of context information (Naudet, Mignon, Lecaque, Hazotte and Groues, 2008), information from social networks (Golbeck and Hendler, 2006), annotations of content with tags (Hung, Huang, Hsu and Wu, 2008), among others. In the video recommendation area, recent works have focused on the annotation of Points of Interest (POI).…”

Section: Introductionmentioning

confidence: 99%

Poi Enhanced Video Recommender System Using Collaboration and Social Networks

Dias

Wives

Roesler

2012

Proceedings of the 8th International Conference on Web Information Systems and Technologies

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Every day, the number of videos available in the world increases. For instance, there is a vast amount of video websites, like Youtube and NetFlix, VOD services, as well as PVR devices that automatically record videos, 24 hours a day. Apparently this situation allows a large possibility of choice for the user, on the other hand, it creates an overload problem, i.e., a difficulty to find the correct content for the user needs. One of the ways to treat such an overload is the use of recommender systems, which filter the content in order to deliver what is most interesting to the user. This paper presents an approach that allows the annotation of points of interest on videos on the Web. Through this, users can mark their most interesting points on the videos. This information can thus be used in conjunction with the user profile and interests to provide recommendations. The differential of this paper is to show how points of interest can be used to enhance video recommender systems and how to design social networks of users with common interest points. 2 RELATED WORKS There are many works about video recommender 717

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