Semantically Enhanced Recommender Systems

Ruiz-Montiel, Manuela; Aldana‐Montes, José F.

doi:10.1007/978-3-642-05290-3_74

Cited by 15 publications

(11 citation statements)

References 8 publications

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“…Classical CF‐based RS generate recommendations based on implicit and explicit ratings of items, which do not consider the attributes of an item. Without taking these attributes into account, the recommendations generated cannot be guaranteed (Wong and Kong 2007; Yolanda et al 2008; Ruiz‐Montiel and Aldana‐Montes 2009). Semantic information about an item consists of the attributes of the item, the relationship between items, and the relationship between items and meta‐information (Resnik 1995; Guo and Lu 2007).…”

Section: The Semantic Relevance Approachmentioning

confidence: 99%

“…The classical recommendation approaches present obstacles in providing recommendations according to these sections of business product categories. Thus, we need to deal with the semantic features of the product categories in the recommendation process, which cannot be well handled in classical CF‐based recommendation approaches (Wong and Kong 2007; Ruiz‐Montiel and Aldana‐Montes 2009). Another obstacle in the application of RS is the great variety of representations of information.…”

Section: Introductionmentioning

confidence: 99%

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A Web‐based Personalized Business Partner Recommendation System Using Fuzzy Semantic Techniques

Shambour

et al. 2012

Computational Intelligence

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The web provides excellent opportunities to businesses in various aspects of development such as finding a business partner online. However, with the rapid growth of web information, business users struggle with information overload and increasingly find it difficult to locate the right information at the right time. Meanwhile, small and medium businesses (SMBs), in particular, are seeking “one‐to‐one” e‐services from government in current highly competitive markets. How can business users be provided with information and services specific to their needs, rather than an undifferentiated mass of information? An effective solution proposed in this study is the development of personalized e‐services. Recommender systems is an effective approach for the implementation of Personalized E‐Service which has gained wide exposure in e‐commerce in recent years. Accordingly, this paper first presents a hybrid fuzzy semantic recommendation (HFSR) approach which combines item‐based fuzzy semantic similarity and item‐based fuzzy collaborative filtering (CF) similarity techniques. This paper then presents the implementation of the proposed approach into an intelligent recommendation system prototype called Smart BizSeeker, which can recommend relevant business partners to individual business users, particularly for SMBs. Experimental results show that the HFSR approach can help overcome the semantic limitations of classical CF‐based recommendation approaches, namely sparsity and new “cold start” item problems.

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Section: The Semantic Relevance Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Web‐based Personalized Business Partner Recommendation System Using Fuzzy Semantic Techniques

Shambour

et al. 2012

Computational Intelligence

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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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“…fuzzy type 1 and fuzzy type 2to tackle vagueness [4], [29]. This emphasises the important role of ontologies in the development of knowledge based system which describes semantic relationships among entities [30].…”

Section: Introductionmentioning

confidence: 99%

A System of Serial Computation for Classified Rules Prediction in Non-Regular Ontology Trees

Ehimwenma¹,

Crowther²,

Beer³

2016

IJAIA

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Objects or structures that are regular take uniform dimensions. Based on the concepts of regular models, our previous research work has developed a system of a regular ontology that models learning structures in a multiagent system for uniform pre-assessments in a learning environment. This regular ontology has led to the modelling of a classified rules learning algorithm that predicts the actual number of rules needed for inductive learning processes and decision making in a multiagent system. But not all processes or models are regular. Thus this paper presents a system of polynomial equation that can estimate and predict the required number of rules of a non-regular ontology model given some defined parameters.

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Semantically Enhanced Recommender Systems

Cited by 15 publications

References 8 publications

A Web‐based Personalized Business Partner Recommendation System Using Fuzzy Semantic Techniques

A Web‐based Personalized Business Partner Recommendation System Using Fuzzy Semantic Techniques

BizSeeker

A System of Serial Computation for Classified Rules Prediction in Non-Regular Ontology Trees

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