Exploiting semantic web technologies for recommender

International Journal of Human-Computer Studies, v. To limit the "cold start" problem by completing the incomplete information through inferences. The Semantic Web project is presented as an extension of the current Web, hoping to become a universal platform for information exchange.

Using social and content-based information in recommendation. This type of system tends to offer poor results when little information is available on the users or when they have heterogeneous tastes Popescule et al, Last updated University Pompeu Fabra.

In a second phase, active consumers are selected, keeping in mind previous recommendations the system does not make recommendations to a client if the number of previous recommendation does not pass a threshold.

Semantic Recommender Systems. Analysis of the state of the topic

Conference on Machine Learning and Cyberneticsv. These systems, just like the social-based systems, are not very reliable when little user information is available. Therefore user profiles have become a key part of efficient filtering in recommender systems, since an inadequate profile may lead to low quality and irrelevant user recommendations.

To allow for the dynamic contextualisation of user preferences in specific domains. Other systems are defined with decentralised structures like P2P networks.

The project is basically based on two ideas: As we have seen, the recommender systems normally use software instead of users for the information filtering tasks.

Exploiting the web of data in model-based recommender systems

This website includes a collaborative recommendation system of movies using FOAF vocabulary as a base for creating a social network of trust. Besides these generic models, there are many others that are defined for more specific domains like the "Foafing the Music" project Celma; Ramirez; Herrera, that recommends music and related news; the system proposed by Middleton and collaborators to recommend scientific articles is based on an automatically generated ontology from information dynamically extracted from various sources online information, user monitoring, feedback, etc.

In recent years many recommender systems have appeared that use Semantic Web technologies and that propose various application solutions in different fields.

Exploiting semantic web technologies for recommender systems: a multi view recommendation engine

To provide a homogeneous representation of both sets of data, an OWL ontology was created in conjunction with the D2RQ mapping technology that allows one to process the data from a relational database, like a virtual RDF graph. Khosravi, Farsani and Nematbakhsh suggest a methodology for personalised recommendations in the context of e-commerce.

Another example of this type of vocabulary is in the model presented by Peis, Herrera-Viedma and Morales-del-Castillowhere a series of RSS fulfil the role of "News Bulletins," within a DSI service for specialised digital libraries.

Ontological or conceptual map based systems, along with other systems that add additional information systems like context-adaptable systems and trust network-based systems.

The folksonomy is used to enrich the knowledge base with descriptions and the categorisation of movie titles, along with the representation of user opinions and interests. This is why many offer an additional filtering level based on a trust network Ziegler, Economic factor-based recommender systems are those that generate recommendations based on cost Resnick et al.

Another criteria consists in distinguishing between centralised systems when the product descriptions and user profiles are stored in a centralised Server or non-centralised Systems generally developed on P2P networks. Traditionally, filtering and recommender systems were classified into three categories relative to the filtering technique used Popescul et al.

For example, the relationship between the service cost and the benefit reported by the client, or the relationship between the bandwidth and size of the file to be downloaded. A music recommendation system based on RSS feeds and user preferences".Towards a Recommender System from Semantic Traces for Decision Aid Ning WANG1, exploited to feed a recommender system.

Exploiting the web of data in model-based recommender systems

Interests of semantic web technologies. Although more and more attention is focused on exploiting implicit information behind data (data mining), these recent. The 7th International Workshop on Intelligent Techniques for Web Personalization & Recommender Systems ITWP Exploiting Semantic Web Technologies for Recommender Systems: A multi View.

SEMANTIC WEB TECHNOLOGIES FOR THE FINANCIAL DOMAIN Rubén Lara1, possibilities for exploiting Semantic Web technologies in the financial domain, the achieved and expected benefits therein, the problems and SEMANTIC WEB TECHNOLOGIES FOR THE FINANCIAL DOMAIN.

Recently, exploiting semantic web technologies such as social recommendations and semantic resources have been investigated. We propose a multi view recommendation engine integrating, in addition of the collaborative recommendations, social and semantic recommendations. Exploiting the web of data in model-based recommender systems.

Full Text: Hafed ZARZOUR, Mohamed Tarek KHADIR, Smart cities based on web semantic technologies, Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, September, Heidelberg, Germany Exploiting the web.

Exploiting the web of data in model-based recommender systems. Full Text: Hafed ZARZOUR, Mohamed Tarek KHADIR, Smart cities based on web semantic technologies, Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, September, Heidelberg, Germany Exploiting the web of data.

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Exploiting semantic web technologies for recommender
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