scholarly journals AKNOBAS: A knowledge-based segmentation recommender system based on intelligent data mining techniques

2012 ◽  
Vol 9 (2) ◽  
pp. 713-740 ◽  
Author(s):  
Alejandro Rodríguez-González ◽  
Javier Torres-Niño ◽  
Enrique Jimenez-Domingo ◽  
Miguel Gomez-Berbis ◽  
Giner Alor-Hernandez
Keyword(s):  
Data Mining ◽  
Competitive Advantage ◽  
Recommender Systems ◽  
Business Model ◽  
Recommender System ◽  
Added Value ◽  
Proof Of Concept ◽  
Clustering Techniques ◽  
Knowledge Based ◽  
Substantial Progress

Recommender Systems have recently undergone an unwavering improvement in terms of efficiency and pervasiveness. They have become a source of competitive advantage in many companies which thrive on them as the technological core of their business model. In recent years, we have made substantial progress in those Recommender Systems outperforming the accuracy and added-value of their predecessors, by using cutting-edge techniques such as Data Mining and Segmentation. In this paper, we present AKNOBAS, a Knowledge-based Segmentation Recommender System, which follows that trend using Intelligent Clustering Techniques for Information Systems. The contribution of this Recommender System has been validated through a business scenario implementation proof-of-concept and provides a clear breakthrough of marshaling information through AI techniques.

On the Importance of Subtext in Recommender Systems

i-com ◽  
2015 ◽  
Vol 14 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Peter Grasch ◽  
Alexander Felfernig
Keyword(s):  
Empirical Study ◽  
Recommender Systems ◽  
Recommender System ◽  
Unsolved Problem ◽  
Preference Elicitation ◽  
User Preferences ◽  
Session Length ◽  
Knowledge Based ◽  
Paralinguistic Information ◽  
Recommendation Strategy

AbstractConversational recommender systems have been shown capable of allowing users to navigate even complex and unknown application domains effectively. However, optimizing preference elicitation remains a largely unsolved problem. In this paper we introduce SPEECHREC, a speech-enabled, knowledge-based recommender system, that engages the user in a natural-language dialog, identifying not only purely factual constraints from the users’ input, but also integrating nuanced lexical qualifiers and paralinguistic information into the recommendation strategy. In order to assess the viability of this concept, we present the results of an empirical study where we compare SPEECHREC to a traditional knowledge-based recommender system and show how incorporating more granular user preferences in the recommendation strategy can increase recommendation quality, while reducing median session length by 46 %.

scholarly journals TEXT SUMMARIZER USING CLUSTERING TECHNIQUES AND ANOMALIES DETECTION ON SVM

2019 ◽  
pp. 1-7
Author(s):  
Oyinloye Oghenerukevwe Elohor ◽  
Adesoji susan ◽  
Akinbohun Folake
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Clustering Algorithm ◽  
Text Summarization ◽  
Support Vector ◽  
Trade Off ◽  
Clustering Techniques ◽  
Svm Classification ◽  
Knowledge Based ◽  
Current Affairs

The study is aimed at developing a text summarizer using clustering and anomalies detection with SVM classification. A text summarization approach is proposed which uses the SVM clustering algorithm. The proposed project can be used to summarize articles from fields as diverse as politics, sports, current affairs, finance and any other explanatory document. However, it does cause a trade-off between domain independence and a knowledge-based summary which would provide data in a form more easily understandable to the user. A bundle of libraries and software’s was utilized for proper text summary of alphanumeric entering. KEYWORDS— Anomalies detection, SVM (support vector machine), clustering, text summarization, data mining

Explicating the business model from a knowledge-based view: nature, structure, imitability and competitive advantage erosion

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jin Chen ◽  
Luyao Wang ◽  
Guannan Qu
Keyword(s):  
Competitive Advantage ◽  
Business Model ◽  
Knowledge Structure ◽  
Firm Boundaries ◽  
Ceteris Paribus ◽  
Content Type ◽  
Knowledge Based ◽  
Strategic Value ◽  
Structured Knowledge ◽  
The Relationship

Purpose The purpose of this paper is to conceptualize the business model (BM) from a knowledge-based view (KBV), to interpret its nature and knowledge structure and to investigate the relationship between its imitability and the erosion of firm’s competitive advantage. Design/methodology/approach Based on a systematic literature review, this study builds an integrated framework to explicate the nature and structure of the BM from a KBV. Moreover, on the analysis of two contrasting cases, the argument concerning the relationship between BM imitability and its strategic value is proposed, analyzed and supported. Findings The main finding of this study is that a BM can be viewed as a structured knowledge cluster that contains explicit and implicit parts. Its imitation is a dynamic process of knowledge diffusion across firm boundaries. Ceteris paribus, with a lower proportion of implicit knowledge, a BM is more likely to be imitated and the adopter’s competitive advantage is more likely to be eroded, and vice versa. Practical implications The proposed framework could provide managers with a deeper understanding of the nature and structure of the BM and help potential adopters develop a successful entry strategy by avoiding BMs that seem profitable but are incapable of maintaining competitive advantage. Originality/value As a complement to previous studies, the research conceptualizes the BM as a “structured knowledge cluster” to explicate its nature and knowledge structure from a KBV. The implicit part of the BM is explored, and its importance for the adopter’s competitive advantage is discussed and verified.

scholarly journals Key Factors for Project Crowdfunding Success: An Empirical Study

2020 ◽  
Vol 12 (2) ◽  
pp. 599 ◽  
Author(s):  
Aladino Fernandez-Blanco ◽  
Joaquin Villanueva-Balsera ◽  
Vicente Rodriguez-Montequin ◽  
Henar Moran-Palacios
Keyword(s):  
Data Mining ◽  
Empirical Study ◽  
Competitive Advantage ◽  
Economic Crisis ◽  
Influential Factors ◽  
Key Factors ◽  
Data Set ◽  
Clustering Techniques ◽  
Banking Institutions ◽  
Financing Problem

Crowdfunding is a response to the financing problem of innovative projects in an environment of severe economic crisis. Its competitive advantage lies in its independence from banking institutions and the distribution of risk among a certain number of funders. Since its inception, the number of successfully completed projects has grown to a point where it has started to suffer a downturn that puts its sustainability at risk. This study concerns this particular period of downturn, in order to identify attributes that characterize it, and to define behavioral stereotypes that may be associated with new projects. On a wide data set from sufficiently contrasted projects, and through the use data mining techniques, we extracted the most influential factors in determining the success or failure of the projects, that will subsequently be grouped together using clustering techniques. Six groups of projects have been identified, each with their own characteristics that define them, two of them clearly guide the projects to success and another one allows the modification its characteristics to move away from failure. This achieved strategy allows us to estimate which potential group would be the result of a new project.

Financial Valuation of a Business Model as an Intangible Asset

2015 ◽  
Vol 11 (4) ◽  
pp. 17-31 ◽  
Author(s):  
Payam Hanafizadeh ◽  
Seyed Saeed Hosseinioun ◽  
Hamid Reza Khedmatgozar
Keyword(s):  
Competitive Advantage ◽  
Business Model ◽  
Bargaining Power ◽  
Business Models ◽  
Intangible Asset ◽  
Venture Capitalists ◽  
Book Value ◽  
Financial Valuation ◽  
Knowledge Based

Knowledge-based economies rely greatly on intangible assets. Based on its features, a business model can be an intangible asset; by posing barriers to imitation, it can create competitive advantage and increase a company's value. Hence, a business model's financial valuation is of great importance. Accordingly, the main objective of the present study is to design a process to valuate business models, using income approach and the concept of competitive advantage. An active corporation engaged in daily deal business was chosen as a case study. Its business model is identified and then valued using the proposed process. The results revealed that the process has reasonable accuracy. Financial valuation of business models is useful for bridging the gap between book value and market value, increasing a firm's ability to raise capital from venture capitalists, improving bargaining power in M&A contracts and providing support in the case of litigation.

scholarly journals TEXT SUMMARIZER USING CLUSTERING TECHNIQUES AND ANOMALIES DETECTION ON SVM

2019 ◽  
pp. 1-7
Author(s):  
Oyinloye Oghenerukevwe Elohor ◽  
Adesoji susan ◽  
Akinbohun Folake
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Clustering Algorithm ◽  
Text Summarization ◽  
Support Vector ◽  
Trade Off ◽  
Clustering Techniques ◽  
Svm Classification ◽  
Knowledge Based ◽  
Current Affairs

The study is aimed at developing a text summarizer using clustering and anomalies detection with SVM classification. A text summarization approach is proposed which uses the SVM clustering algorithm. The proposed project can be used to summarize articles from fields as diverse as politics, sports, current affairs, finance and any other explanatory document. However, it does cause a trade-off between domain independence and a knowledge-based summary which would provide data in a form more easily understandable to the user. A bundle of libraries and software’s was utilized for proper text summary of alphanumeric entering. KEYWORDS— Anomalies detection, SVM (support vector machine), clustering, text summarization, data mining

IMPROVING THE EFFECTIVENESS OF KNOWLEDGE BASED RECOMMENDER SYSTEMS USING INCOMPLETE LINGUISTIC PREFERENCE RELATIONS

2008 ◽  
Vol 16 (supp02) ◽  
pp. 33-56 ◽  
Author(s):  
LUIS MARTÍNEZT ◽  
LUIS G. PÉREZ ◽  
MANUEL BARRANCO ◽  
MACARENA ESPINILLA
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Web Sites ◽  
Linguistic Information ◽  
Preference Relations ◽  
New Methods ◽  
Knowledge Based ◽  
Lack Of Information

In the e-commerce arena new methods and tools have been recently developed to improve and customize the e-commerce web sites, according to users' necessities and preferences, that are usually vague and uncertain. The most successful tool in this field has been the Recommender Systems. Their aim is to assist e-shops customers to find out the most suitable products by using recommendations. Sometimes, these systems face situations where there is a lack of information or the information is vague or imprecise that yield unsuccessful results. Although several solutions have been proposed, they still present some limitations. In this paper, we present a Knowledge-Based Recommender System that manages and models the uncertainty related to users' preferences by using linguistic information. This system will overcome the problem of lack of information by computing recommendations through completing incomplete linguistic preference relations provided by the users.

A Recommendation Method Based on Semantic Similarity and Complementarity Using Weighted Taxonomy: A Case on Construction Materials Dataset

2018 ◽  
Vol 17 (01) ◽  
pp. 1850010
Author(s):  
Karamollah Bagherifard ◽  
Mohsen Rahmani ◽  
Vahid Rafe ◽  
Mehrbakhsh Nilashi
Keyword(s):  
Data Mining ◽  
Recommender Systems ◽  
Semantic Similarity ◽  
Recommender System ◽  
Construction Materials ◽  
Similar Degree ◽  
Web Pages ◽  
New Approach ◽  
Main Components ◽  
The Relationship

Products and web pages are the main components of the e-commerce data knowledge and the relationship among them is an important issue to be highly considered in recommender systems. This study aims to focus on the similarity and complementarity relationships among the products that have wide applications in the recommender systems. In the previously proposed methods, products and their relationships were revealed using taxonomy and “IS-A” relationship. In addition, the similarity and complementarity calculations were conducted based on edge computation by assigning a similar degree to any edge. More specifically, the children of a concept in the taxonomy was supported by a similar father’s “IS-A” degree. In contrast, this study provides a new approach based on ontology, data mining, and automatic discovering algorithms for the relationships with different degrees for the edges among the concepts. Accordingly, these relationships are initialised according to the “IS-A” degree. With regard to this weighted taxonomy, the semantic similarity and complementarity are measured based on concept distance. In addition, the proposed recommender system is item-based, which uses semantic similarity and complementarity. The required data for the present study were collected from construction materials supplier. The results illustrated that our proposed method is effective for construction materials recommendation.

The Knowledge Creation Organization: The Case of Alphana

2012 ◽  
pp. 78-90
Author(s):  
Thang Nguyen Ngoc
Keyword(s):  
Competitive Advantage ◽  
Knowledge Creation ◽  
Future Research ◽  
Sustainable Competitive Advantage ◽  
Knowledge Based ◽  
The Future

Knowledge and the capability to create and utilize knowledge today are consid- ered to be the most important sources of a firm’s sustainable competitive advantage. This paper aims to advance understanding of the knowledge creation of firm in Vietnam by studying Alphanam Company. The case illustrates how knowledge- based management pursues a vision for the future based on ideals that consider the relationships of people in society. The finding shows that the case succeeded because of their flexibility and mobility to keep meeting to the changing needs of the customers or stakeholders. The paper also provided some suggestions for future research to examine knowledge-based management of the companies in a different industry segments and companies originating in other countries

How Sweet and Ripe are the Fruits? Data Mining Techniques for Classifying and Predicting ‘Quick-Wins’ Direct Capital Investment in Indonesia as One Approach to Business intelligence Orientation and Knowledge Management Scenarios of Indonesian Enterprises

2019 ◽  
Vol 1 (1) ◽  
pp. 121-131
Author(s):  
Ali Fauzi
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Direct Investment ◽  
Capital Investment ◽  
Export Market ◽  
Added Value ◽  
Knowledge Generation ◽  
Management Scenarios ◽  
Data Mining Techniques ◽  
Knowledge Based Economy

The existence of big data of Indonesian FDI (foreign direct investment)/ CDI (capital direct investment) has not been exploited somehow to give further ideas and decision making basis. Example of data exploitation by data mining techniques are for clustering/labeling using K-Mean and classification/prediction using Naïve Bayesian of such DCI categories. One of DCI form is the ‘Quick-Wins’, a.k.a. ‘Low-Hanging-Fruits’ Direct Capital Investment (DCI), or named shortly as QWDI. Despite its mentioned unfavorable factors, i.e. exploitation of natural resources, low added-value creation, low skill-low wages employment, environmental impacts, etc., QWDI , to have great contribution for quick and high job creation, export market penetration and advancement of technology potential. By using some basic data mining techniques as complements to usual statistical/query analysis, or analysis by similar studies or researches, this study has been intended to enable government planners, starting-up companies or financial institutions for further CDI development. The idea of business intelligence orientation and knowledge generation scenarios is also one of precious basis. At its turn, Information and Communication Technology (ICT)’s enablement will have strategic role for Indonesian enterprises growth and as a fundamental for ‘knowledge based economy’ in Indonesia.

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