Semantic-Enhanced Personalized Recommender System

Wang, Ruiqin; Kong, Fanrong

doi:10.1109/icmlc.2007.4370858

Cited by 40 publications

(17 citation statements)

References 4 publications

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“…Mobasher, Jin and Zhou have taken advantage of an item ontology, which is part of the schema for a relational database, in order to compute similarity correlations between items [2]. Wang and Kong also use an ontology to calculate the correlations between items, and they use a very similar algorithm to the one we present in this paper; but they do not use the ontology to infer the semantic features, since they explicitly specify them [4]. In [5] and [6], the authors also propose the use of semantic descriptions of items and user profiles.…”

Section: Related Workmentioning

confidence: 94%

Semantically Enhanced Recommender Systems

Ruiz-Montiel

Aldana‐Montes

2009

Lecture Notes in Computer Science

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Abstract. Recommender Systems have become a significant area in the context of web personalization, given the large amount of available data. Ontologies can be widely taken advantage of in recommender systems, since they provide a means of classifying and discovering of new information about the items to recommend, about user profiles and even about their context. We have developed a semantically enhanced recommender system based on this kind of ontologies. In this paper we present a description of the proposed system.

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Section: Related Workmentioning

confidence: 94%

Semantically Enhanced Recommender Systems

Ruiz-Montiel

Aldana‐Montes

2009

Lecture Notes in Computer Science

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“…Regarding existing semantics-based collaborative (and hybrid) systems [33,13,29,40], their recommendations were not compared against ours because their philosophies are essentially different. Specifically, some recommendations selected by collaborative approaches would go unnoticed to our strategy (because we do not consider the profiles of other users), and vice versa (because existing approaches disregard the relationships discovered by our semantic reasoning techniques).…”

Section: Preliminary Evaluationmentioning

confidence: 98%

“…In order to fight overspecialization, researchers devised collaborative filtering [36,25,29] -whose basic idea is to move beyond the experience of an individual user's profile and instead draw on the experiences of a community of like-minded users (his/her neighbors), and even they combined content-based and collaborative filtering in hybrid approaches [6,22,33,13,40]. Even though collaborative (and hybrid) approaches mitigate the effects of overspecialization by considering the interests of other users, they bring in new limitations, such as the sparsity problem (related to difficulties to select each individual's neighborhood when the knowledge about the users' preferences is sparse), privacy concerns bound to the confidentiality of the users' personal data, and scalability problems stemmed from the management of many user profiles (instead of just one profile like in content-based approaches).…”

Section: Introductionmentioning

confidence: 99%

Exploring synergies between content-based filtering and Spreading Activation techniques in knowledge-based recommender systems

Blanco‐Fernández¹,

López‐Nores²,

Gil‐Solla³

et al. 2011

Information Sciences

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“…However, these implicit and explicit ratings do not consider the attributes of an item. Without taking these attributes into account, the recommendation generated cannot be guaranteed (Montiel and Montes, 2009;Wang and Kong, 2007;Blanco-Fernández et al, 2008). On the other hand, semantic information about an item consists of the attributes of INTR 20,3 the item, the relationship of the item to other items, and other meta-information.…”

Section: A Product Semantic Relevance Modelmentioning

confidence: 99%

BizSeeker

Shambour

et al. 2010

Internet Research

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to develop a hybrid semantic recommendation system to provide personalized government to business (G2B) e-services, in particular, business partner recommendation e-services for Australian small to medium enterprises (SMEs). Design/methodology/approach -The study first proposes a product semantic relevance model. It then develops a hybrid semantic recommendation approach which combines item-based collaborative filtering (CF) similarity and item-based semantic similarity techniques. This hybrid approach is implemented into an intelligent business-partner-locator recommendation-system prototype called BizSeeker. Findings -The hybrid semantic recommendation approach can help overcome the limitations of existing recommendation techniques. The recommendation system prototype, BizSeeker, can recommend relevant business partners to individual business users (e.g. exporters), which therefore will reduce the time, cost and risk of businesses involved in entering local and international markets. Practical implications -The study would be of great value in e-government personalization research. It would facilitate the transformation of the current G2B e-services into a new stage wherein the e-government agencies offer personalized e-services to business users. The study would help government policy decision-makers to increase the adoption of e-government services. Originality/value -Providing personalized e-services by e-government can be seen as an evolution of the intentions-based approach and will be one of the next directions of government e-services. This paper develops a new recommender approach and systems to improve personalization of government e-services.

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Semantic-Enhanced Personalized Recommender System

Cited by 40 publications

References 4 publications

Semantically Enhanced Recommender Systems

Semantically Enhanced Recommender Systems

Exploring synergies between content-based filtering and Spreading Activation techniques in knowledge-based recommender systems

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