scholarly journals An Interactive Intelligent Search Engine Model Research Based on User Information Preference

2006 ◽  
Author(s):  
Dan Meng ◽  
Xu Huang
Keyword(s):  
Search Engine ◽  
Engine Model ◽  
Intelligent Search ◽  
Information Preference ◽  
Model Research

MARS

Web Mining ◽  
2011 ◽  
pp. 99-118 ◽  
Author(s):  
Xiannong Meng ◽  
Zhixiang Chen
Keyword(s):  
Search Engine ◽  
Relevance Feedback ◽  
Adaptive Algorithm ◽  
User Preference ◽  
Adaptive Refinement ◽  
Search Performance ◽  
Web Searches ◽  
Expansion Strategy ◽  
Information Preference ◽  
Feedback Algorithm

This chapter reports the project MARS (Multiplicative Adaptive Refinement Search), which applies a new multiplicative adaptive algorithm for user preference retrieval to Web searches. The new algorithm uses a multiplicative query expansion strategy to adaptively improve and reformulate the query vector to learn users’ information preference. The algorithm has provable better performance than the popular Rocchio’s similarity-based relevance feedback algorithm in learning a user preference that is determined by a linear classifier with a small number of non-zero coefficients over the real-valued vector space. A meta-search engine based on the aforementioned algorithm is built, and analysis of its search performance is presented.

Development of a Novel Compressed Index-Query Web Search Engine Model

2011 ◽  
Vol 6 (3) ◽  
pp. 39-56 ◽  
Author(s):  
Hussein Al-Bahadili ◽  
Saif Al-Saab
Keyword(s):  
Data Compression ◽  
Search Engine ◽  
Web Search ◽  
Server Side ◽  
Engine Model ◽  
Web Search Engine ◽  
Index Index ◽  
Compression Layer ◽  
Compressed Index ◽  
Space Requirements

In this paper, the authors present a description of a new Web search engine model, the compressed index-query (CIQ) Web search engine model. This model incorporates two bit-level compression layers implemented at the back-end processor (server) side, one layer resides after the indexer acting as a second compression layer to generate a double compressed index (index compressor), and the second layer resides after the query parser for query compression (query compressor) to enable bit-level compressed index-query search. The data compression algorithm used in this model is the Hamming codes-based data compression (HCDC) algorithm, which is an asymmetric, lossless, bit-level algorithm permits CIQ search. The different components of the new Web model are implemented in a prototype CIQ test tool (CIQTT), which is used as a test bench to validate the accuracy and integrity of the retrieved data and evaluate the performance of the proposed model. The test results demonstrate that the proposed CIQ model reduces disk space requirements and searching time by more than 24%, and attains a 100% agreement when compared with an uncompressed model.

scholarly journals Towards Personalized Medication

2021 ◽  
Author(s):  
Michal Huptych ◽  
Jiri Potucek ◽  
Lenka Lhotská
Keyword(s):  
Therapeutic Drug Monitoring ◽  
Search Engine ◽  
Drug Monitoring ◽  
Literature Search ◽  
Relevant Literature ◽  
Therapeutic Drug ◽  
Pharmacokinetics And Pharmacodynamics ◽  
Intelligent Search ◽  
Essential Step ◽  
Project Data

The paper describes some aspects of precision medicine and shows the importance of pharmacokinetics and pharmacodynamics for the therapeutic drug monitoring and model-informed precision dosing. A key element in the design of the pharmacokinetics and pharmacodynamics (PKPD) models is relevant literature search that represents an essential step in the procurement and validation of a new drug. Available search engine resources do not offer specific functionalities that are required for efficient and relevant search in reliable literature sources. We present a prototype of such an intelligent search engine and show its results on real project data.

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