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Application of Case Based Recommender System in Investment Sector and Investment Activity Selection to New Investors: in The Case of Ethiopia.

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dc.contributor.advisor Kebede, Gashaw (PhD)
dc.contributor.author Chanie, Yibeltal
dc.date.accessioned 2018-12-04T13:38:27Z
dc.date.available 2018-12-04T13:38:27Z
dc.date.issued 2013-06
dc.identifier.uri http://localhost:80/xmlui/handle/123456789/14839
dc.description.abstract Investment is a commitment of funds, directly or indirectly, to one or more assets with the expectation to enhance future wealth. Each and every person can be specifically differentiated on various parameters in investment selection decision. Prior to purchasing any investment product or service, it is important that investors fully think about their unique needs and overall financial situation in order to determine whether the investment or service is right for them. However, investment advice in Ethiopia has different problems. Among these problems, there are no sufficient and knowledgeable experts to give advice to investors on investment sector and investment activity selection. And also there is no consistency in advising system from expert to expert. As a result, majority of investors drop out of the investment projects or are not successful. The aim of this research is to develop case based recommender system for investment sector and investment activity selection that assists investment experts and investors to make timely decisions. To develop case based recommender system for investment sector and investment activity selection, important knowledge was acquired through interview and document analysis. Twelve domain experts and four investors were interviewed to elicit the required knowledge about investment sector and investment activity. The acquired knowledge was modeled using hierarchical structure. The acquired knowledge was represented using feature value case base representation and implemented using jCOLIBRI programming tool. The main data source (case base) used to develop Case based recommender system for investment sector and investment activity selection (CBRISAIAS) is previous investor cases from EIA. Nearest neighbor retrieval algorithm is used to measure the similarity of new case (query) with cases in the case base. As a result, if there is a similarity between the new case and the existing case the system assigns the solution (recommended investment sector and investment activity) of previous case as a solution to new case. To determine the applicability of the prototype system in the domain area, the system has been evaluated by the domain experts and investors through visual interaction based on the criteria of easiness to use, time efficiency, applicability in the domain area and providing correct recommendation.According to the evaluation through user acceptance 82% system performance is obtained from domain experts and 84% system performance is obtained from investors. And also the performance of the prototype is measured using recall, precision and accuracy measures, where the system achieves 85% recall, 64% precision and 87% accuracy. Further study should be conducted that by adding other important attributes that have an influence in investment sector and investment activity selection. And also further study conducted by using hybrid system of rule based and case based recommender system to enhance the performance of the system, because the hybrid system eliminates the limitation of case based and rule based recommender system. Finally further research can be done by developing the case based recommender system using different local languages for the purpose of investors can easily communicate with the system by using their own languages. en_US
dc.language.iso en en_US
dc.publisher Addis Ababa University en_US
dc.subject Application of Case Based Recommender System en_US
dc.title Application of Case Based Recommender System in Investment Sector and Investment Activity Selection to New Investors: in The Case of Ethiopia. en_US
dc.type Thesis en_US


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