Application of Fuzzy Ontology to Information Retrieval for Electronic Commerce

2008 ◽  
Author(s):  
Jun Zhai ◽  
Lixin Shen ◽  
Yiduo Liang ◽  
Jiatao Jiang
Keyword(s):  
Information Retrieval ◽  
Electronic Commerce ◽  
Fuzzy Ontology

E-Commerce Personalized Recommendation

2014 ◽  
Vol 989-994 ◽  
pp. 4996-4999 ◽  
Author(s):  
Yan Zhang
Keyword(s):  
Information Retrieval ◽  
Electronic Commerce ◽  
Association Rule ◽  
Information Overload ◽  
Rapid Development ◽  
Personalized Recommendation ◽  
Application Layer ◽  
Advantages And Disadvantages ◽  
Theory Research ◽  
Content Based Filtering

With the rapid development of electronic commerce, the problem of "information overload" leads to the difficulty that user can't search the required goods effectively , personalized recommendation technology has been applied in e-commerce and popularization. By using the method of qualitative analysis of the current e-commerce site, the paper compare the information retrieval, association rule, content-based filtering and collaborative filtering four main recommendation technologies and analysis the advantages and disadvantages in the application layer, the recommendation technologies are introduced to review e-commerce research hot topic in the field of personalized recommendation, and analysis the current domestic e-commerce personalized recommendation theory research and application status, finally propose the challenges faced by e-commerce personalized recommendation domain.

An Enhanced Fuzzy Information Retrieval Model Based on Linguistics

2014 ◽  
Vol 519-520 ◽  
pp. 853-856
Author(s):  
Zeinab E. Al-Arab ◽  
Ahmed M. Gadallah ◽  
Hesham M. Hefny
Keyword(s):  
Information Retrieval ◽  
Ranking Algorithm ◽  
Retrieval Model ◽  
Fuzzy Information ◽  
Model Based ◽  
Fuzzy Ontology ◽  
Confidence Degree ◽  
Subjective View ◽  
Fuzzy Information Retrieval

The paper proposes a linguistic based fuzzy ontology information retrieval model. The model deals with linguistic based queries in multi domains. Such linguistics are user defined, reflecting his subjective view. The model also proposes a ranking algorithm that ranks the set of relevant documents according to some criteria such as their relevance degree, confidence degree, and updating degree.

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