Ontology-Based Personalised and Context-Aware Recommendations of News Items

Cantador, Iván; Bellogín, Alejandro; Castells, Pablo

doi:10.1109/wiiat.2008.204

Cited by 76 publications

(54 citation statements)

References 4 publications

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“…AMAYA also uses an ontology-based content categorization scheme to map user preferences to entities to recommend. News@hand [22] is an hybrid personalized and context aware recommender system, which retrieves news via RSS feed and annotates by using system domain ontologies. User context is represented by a weighted set of classes from the domain ontology.…”

Section: B Ontology Based Recommender Systemmentioning

confidence: 99%

An Ontology-based Contextual Pre-filtering Technique for Recommender Systems

Karpus

Vagliano

Goczyła

et al. 2016

Annals of Computer Science and Information Systems

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Abstract-Context-aware Recommender Systems aim to provide users with the most adequate recommendations for their current situation. However, an exact context obtained from a user could be too specific and may not have enough data for accurate rating prediction. This is known as the data sparsity problem. Moreover, often user preference representation depends on the domain or the specific recommendation approach used. Therefore, a big effort is required to change the method used. In this paper we present a new approach for contextual pre-filtering (i.e. using the current context to select a relevant subset of data). Our approach can be used with existing recommendation algorithms. It is based on two ontologies: Recommender System Context ontology, which represents the context, and Contextual Ontological User Profile ontology, which represents user preferences. We evaluated our approach through an offline study which showed that when used with well-known recommendation algorithms it can significantly improve the accuracy of prediction.

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Section: B Ontology Based Recommender Systemmentioning

confidence: 99%

An Ontology-based Contextual Pre-filtering Technique for Recommender Systems

Karpus

Vagliano

Goczyła

et al. 2016

Annals of Computer Science and Information Systems

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“…In this paper, a model has been evaluated which will take care of the users long-term interest as well as the current interest. For safeguarding the long-term interest of the user, a model has been developed which can personalize the order of visibility of news items, for protecting the current interest, a model which can reorder the news items has been developed [23].…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Ontology based KM: A Review

Kumar¹,

Tiwari²

2014

IJCA

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Knowledge management helps organizations to exploit and develop resources, improve their competitiveness and develop sustainable competitive advantage. Main focus of the research in KM is on theoretical concepts, approaches, and sustaining tools for human knowledge management. KM plans to manage knowledge by organizing formal and direct method to manage organizational knowledge in the workplace. There are many literatures that have shown a number of KM approaches developed with the purpose of managing organizational knowledge. However, there is a need to focus on study of ontology based collaborative filtering in the existing business environment. This paper reviews ontology-based KM approaches. The primary goal of this work is to understand and evaluate each approach by analyzing and using.

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“…Among other approaches, News@hand offers personalized [23], context-aware [9] and collaborative multi-facet recommendations [7]. Configurations and combinations of the above recommendation models are model feature characteristics included in the study presented herein.…”

Section: Architecturementioning

confidence: 99%

“…A runtime context is represented in our approach [9,23] as a set of weighted concepts from the domain ontologies. This set is obtained by collecting the concepts that have been involved in the interaction of the user (e.g., accessed items) during a session.…”

Section: Context-aware Recommendationsmentioning

confidence: 99%

“…We have tested this proposal with News@hand [8], a news recommender system that suggests news articles according to several recommendation models, namely: 1) a personalized content-based model, the item suggestions from which are based on longterm user profiles [9], 2) a context-aware model that exploits user preferences which are not expressed in the user profile, but can be implicitly detected in the current user recommendation context [23], and 3) a collaborative model that finds and exploits implicit interest relations among users to provide enriched recommendations [7].…”

Section: Introductionmentioning

confidence: 99%

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Discerning Relevant Model Features in a Content-based Collaborative Recommender System

Bellogín

Cantador

Castells

et al. 2010

Preference Learning

Self Cite

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Abstract. Recommender systems suggest users information items they may be interested in. User profiles or usage data are compared with some reference characteristics, which may belong to the items (content-based approach), or to other users in the same context (collaborative filtering approach). These items are usually presented as a ranking, where the more relevant an item is predicted to be for a user, the higher it appears in the ranking. In this scenario, a preferential order has to be inferred, and therefore, preference learning methods can be naturally helpful. The relevant recommendation model features for the learningbased enhancements explored in this work comprise parameters of the recommendation algorithms, and user-related attributes. In the researched approach, machine learning techniques are used to discover which model features are relevant in providing accurate recommendations. The assessment of relevant model features, which is the focus of this paper, is envisioned as the first step in a learning cycle in which improved recommendation models are produced and executed after the discovery step, based on the findings that result from it.

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Ontology-Based Personalised and Context-Aware Recommendations of News Items

Abstract: Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in:

Cited by 76 publications

References 4 publications

An Ontology-based Contextual Pre-filtering Technique for Recommender Systems

An Ontology-based Contextual Pre-filtering Technique for Recommender Systems

Ontology based KM: A Review

Discerning Relevant Model Features in a Content-based Collaborative Recommender System

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