A web-page recommender system via a data mining framework and the Semantic Web concept

2006 ◽  
Vol 27 (4) ◽  
pp. 298 ◽  
Author(s):  
Choochart Haruechaiyasak ◽  
Mei Ling Shyu ◽  
Shu Ching Chen
Keyword(s):  
Data Mining ◽  
Semantic Web ◽  
Recommender System ◽  
Web Page

Data Mining Techniques to Build A Recommender System

2021 ◽  
Author(s):  
Alicia Huidobro Espejel ◽  
Francisco J. Cantu-Ortiz
Keyword(s):  
Data Mining ◽  
Recommender System ◽  
Data Mining Techniques

scholarly journals Knowledge-based Data Mining Using Semantic Web

IERI Procedia ◽  
2014 ◽  
Vol 7 ◽  
pp. 113-119 ◽  
Author(s):  
Sumaiya Kabir ◽  
Shamim Ripon ◽  
Mamunur Rahman ◽  
Tanjim Rahman
Keyword(s):  
Data Mining ◽  
Semantic Web ◽  
Knowledge Based

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

2021 ◽  
Author(s):  
Zainab Al-Zanbouri
Keyword(s):  
Data Mining ◽  
Energy Consumption ◽  
Web Services ◽  
Recommender System ◽  
Energy Efficient ◽  
Contextual Information ◽  
Three Dimensions ◽  
Context Aware ◽  
Rating Data ◽  
Recommendation Accuracy

Currently, there is a big increase in the usage of data analytics applications and services because of the growth in the data produced from different sources. The QoS properties such as response time and latency of these services are important factors to decide which services to select. As a result of IT expansion, energy consumption has become a big issue. Therefore, establishing a QoS-based web service recommender system that considers energy consumption as one of the essential QoS properties represents a significant step towards selecting the energy efficient web services. This dissertation presents an experimental study on energy consumption levels and latency behavior collected from a set of data mining web services running on different datasets. Our study shows that there is a strong relation between the dataset properties and the QoS properties. Based on the findings from this study, a recommender system is built which considers three dimensions (user, service, dataset). The energy consumption values of candidate services invoked by specific users can be predicted for a given dataset. Afterwards, these services can be ranked according to their predicted energy values and presented to users. We propose three approaches to build our recommender system and we treat it as a context-aware recommendation problem. The dataset is considered as contextual information and we use a context-aware matrix factorization model to predict energy values. In the first approach, we adopt the pre-filtering model where the contextual information serves as a query for filtering relevant rating data. In the second approach, we propose a new method for the pre-filtering implementation. Finally, in the last approach, we adopt the contextual modeling method and we explore different ways of representing dataset information as contextual factors to investigate their impacts on the recommendation accuracy. We compare the proposed approaches with the baseline approaches and the results show the effectiveness of the proposed ones. Also, we compare the performance of the three approaches to discover the best-fit approach when being measured using different metrics. Both prediction and recommendation accuracy of the proposed approaches are significantly better than the baseline models.

Intelligent Techniques in Recommender Systems and Contextual Advertising

2013 ◽  
pp. 105-128 ◽  
Author(s):  
Giuliano Armano ◽  
Alessandro Giuliani ◽  
Eloisa Vargiu
Keyword(s):  
Recommender System ◽  
Information Filtering ◽  
Relevant Information ◽  
Web Page ◽  
Contextual Advertising ◽  
Generic Hybrid ◽  
Unified View ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
The Web

Information Filtering deals with the problem of selecting relevant information for a given user, according to her/his preferences and interests. In this chapter, the authors consider two ways of performing information filtering: recommendation and contextual advertising. In particular, they study and analyze them according to a unified view. In fact, the task of suggesting an advertisement to a Web page can be viewed as the task of recommending an item (the advertisement) to a user (the Web page), and vice versa. Starting from this insight, the authors propose a content-based recommender system based on a generic solution for contextual advertising and a hybrid contextual advertising system based on a generic hybrid recommender system. Relevant case studies have been considered (i.e., a photo recommender and a Web advertiser) with the goal of highlighting how the proposed approach works in practice. In both cases, results confirm the effectiveness of the proposed solutions.

Metaphorical Communication about Nature

2014 ◽  
pp. 208-224
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Semantic Web ◽  
Data Visualization ◽  
Knowledge Visualization ◽  
Art And Science ◽  
Learning And Teaching ◽  
Mathematical Equations ◽  
Abstract Ideas

Metaphors are present in our thoughts and make invisible concepts perceivable. The metaphorical way of perceptual imaging is discussed in this chapter, particularly the use of art and graphic metaphors for concept visualization. We may describe with metaphors the structure and the relations among several kinds of data. Metaphors may represent mathematical equations or geometrical curves and thus make abstract ideas visible. Most metaphors originate from biology-inspired thinking. Nature-derived metaphors support data visualization, information and knowledge visualization, data mining, Semantic Web, swarm computing, cloud computing, and serve as the enrichment of interdisciplinary models. This chapter examines examples of combining metaphorical visualization with artistic principles, and then describes the metaphorical way of learning and teaching with art and graphic metaphors aimed at improving one’s power of conveying meaning, integrating art and science, and visualizing knowledge.

Ontologies in the Health Field

2011 ◽  
pp. 37-56 ◽  
Author(s):  
Michel Simonet ◽  
Radja Messai ◽  
Gayo Diallo
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Semantic Web ◽  
Knowledge Bases ◽  
Health Data ◽  
Efficient Manner ◽  
Common Definition ◽  
Ontology Design ◽  
Definition Of ◽  
Health Field

Health data and knowledge had been structured through medical classifications and taxonomies long before ontologies had acquired their pivot status of the Semantic Web. Although there is no consensus on a common definition of an ontology, it is necessary to understand their main features to be able to use them in a pertinent and efficient manner for data mining purposes. This chapter introduces the basic notions about ontologies, presents a survey of their use in medicine and explores some related issues: knowledge bases, terminology, and information retrieval. It also addresses the issues of ontology design, ontology representation, and the possible interaction between data mining and ontologies.

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