scholarly journals An information retrieval model based on simple Bayesian networks

2003 ◽  
Vol 18 (2) ◽  
pp. 251-265 ◽  
Author(s):  
Silvia Acid ◽  
Luis M. De Campos ◽  
Juan M. Fernández-Luna ◽  
Juan F. Huete
Keyword(s):  
Information Retrieval ◽  
Bayesian Networks ◽  
Retrieval Model ◽  
Model Based

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.

PLSA-Based Personalized Information Retrieval with Network Regularization

2019 ◽  
Vol 12 (1) ◽  
pp. 105-116
Author(s):  
Qiuyu Zhu ◽  
Dongmei Li ◽  
Cong Dai ◽  
Qichen Han ◽  
Yi Lin
Keyword(s):  
Information Retrieval ◽  
Semantic Analysis ◽  
Topic Model ◽  
Rapid Development ◽  
Probabilistic Latent Semantic Analysis ◽  
Retrieval Model ◽  
User Interest ◽  
Model Based ◽  
User Query ◽  
Academic Information

With the rapid development of the Internet, the information retrieval model based on the keywords matching algorithm has not met the requirements of users, because people with various query history always have different retrieval intentions. User query history often implies their interests. Therefore, it is of great importance to enhance the recall ratio and the precision ratio by applying query history into the judgment of retrieval intentions. For this sake, this article does research on user query history and proposes a method to construct user interest model utilizing query history. Coordinately, the authors design a model called PLSA-based Personalized Information Retrieval with Network Regularization. Finally, the model is applied into academic information retrieval and the authors compare it with Baidu Scholar and the personalized information retrieval model based on the probabilistic latent semantic analysis topic model. The experiment results prove that this model can effectively extract topics and retrieves back results more satisfied for users' requirements. Also, this model improves the effect of retrieval results apparently. In addition, the retrieval model can be utilized not only in the academic information retrieval, but also in the personalized information retrieval on microblog search, associate recommendation, etc.

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