A generalized site ranking model for Web IR

2004 ◽  
Author(s):  
C. Ding ◽  
C.-H. Chi
Keyword(s):  
Ranking Model ◽  
Web Ir ◽  
Site Ranking

Assessing user simulation for dialog systems using human judges and automatic evaluation measures

2011 ◽  
Vol 17 (4) ◽  
pp. 511-540 ◽  
Author(s):  
HUA AI ◽  
DIANE LITMAN
Keyword(s):  
Gold Standard ◽  
System Development ◽  
Automatic Evaluation ◽  
Dialog Systems ◽  
Evaluation Measures ◽  
Assessment Study ◽  
User Simulation ◽  
Dialog System ◽  
Ranking Model

AbstractWhile different user simulations are built to assist dialog system development, there is an increasing need to quickly assess the quality of the user simulations reliably. Previous studies have proposed several automatic evaluation measures for this purpose. However, the validity of these evaluation measures has not been fully proven. We present an assessment study in which human judgments are collected on user simulation qualities as the gold standard to validate automatic evaluation measures. We show that a ranking model can be built using the automatic measures to predict the rankings of the simulations in the same order as the human judgments. We further show that the ranking model can be improved by using a simple feature that utilizes time-series analysis.

scholarly journals A ranking model for the greedy algorithm and discrete convexity

2010 ◽  
Vol 132 (1-2) ◽  
pp. 393-407 ◽  
Author(s):  
Ulrich Faigle ◽  
Walter Kern ◽  
Britta Peis
Keyword(s):  
Greedy Algorithm ◽  
Discrete Convexity ◽  
Ranking Model ◽  
The Greedy Algorithm

A qualitative attraction ranking model for personalized recommendations

2018 ◽  
Vol 9 (1) ◽  
pp. 2-13
Author(s):  
Thara Angskun ◽  
Jitimon Angskun
Keyword(s):  
Recommendation System ◽  
Theoretical Implication ◽  
Personalized Recommendation ◽  
The Novel ◽  
Analytic Hierarchy ◽  
Content Type ◽  
Tourist Attractions ◽  
Ranking Model ◽  
Travel Decisions ◽  
Hierarchy Process

Purpose This paper aims to find a way to personalize attraction recommendations for travelers. The research objective is to find a more accurate way to suggest new attractions to each traveler based on the opinions of other like-minded travelers and the traveler’s preferences. Design/methodology/approach To achieve the goal, developers have created a personalized system to generate attraction recommendations. The system considers an individual traveler’s preferences to construct a qualitative attraction ranking model. The new ranking model is the result of blending two processes: K-means clustering and the analytic hierarchy process (AHP). Findings The performance of the developed recommendation system has been assessed by measuring the accuracy and scalability of the ranking model of the system. The experimental results indicate that the ranking model always returns accurate results independent of the number of attractions and the number of travelers in each cluster. The ranking model has also proved to be scalable because the processing time is independent of the numbers of travelers. Additionally, the results reveal that the overall system usability is at a very satisfactory level. Research limitations/implications The main theoretical implication is that integrating the processes of K-means and AHP techniques enables a new qualitative ranking model for personalized recommendations that deliver only high-quality attractions. However, the designed recommendation system has some limitations. First, it is necessary to manually update information about the new tourist attractions. Second, the overall response time depends on the internet bandwidth and latency. Practical implications This research contributes to the tourism business and individual travelers by introducing an accurate and scalable way to suggest new attractions to each traveler. The potential benefit includes possible increased revenue for travel agencies that offer personalized package tours and support individual travelers to make the final travel decisions. The designed system could also integrate with itinerary planning systems to plot out a journey that pinpoints what travelers will most enjoy. Originality/value This research proposes a design and implementation of a personalized recommendation system based on the qualitative attraction ranking model introduced in this article. The novel ranking model is designed and developed by integrating K-means and AHP techniques, which has proved to be accurate and scalable.

MF-Re-Rank: A modality feature-based Re-Ranking model for medical image retrieval

2018 ◽  
Vol 69 (9) ◽  
pp. 1095-1108 ◽  
Author(s):  
Hajer Ayadi ◽  
Mouna Torjmen-Khemakhem ◽  
Mariam Daoud ◽  
Jimmy Xiangji Huang ◽  
Maher Ben Jemaa
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Medical Image Retrieval ◽  
Ranking Model ◽  
Feature Based

scholarly journals A Heuristic Ranking of Different Characteristic Mining Based Mathematical Formulae Retrieval Models

2019 ◽  
Vol 9 (1) ◽  
pp. 893-901
Keyword(s):  
Performance Metrics ◽  
Text File ◽  
Retrieval Models ◽  
Retrieval Time ◽  
Retrieval Algorithms ◽  
Ranking Model ◽  
Sensitivity Specificity

The significant difficulty in the present circumstances is how to classify the math related keywords from a given text file and group them in one math file. Through this article a heuristic ranking model was developed and was evaluated on different mathematical formulae retrieval algorithms based on Characteristic mining. Our proposed heuristic ranking model was developed using the performance metrics of exiting retrieval algorithms such as NMF clustering, Levenstein distance, Sequence matcher, Fuzzy-wuzzy and Tensorflow. Performance metrics such as sensitivity, specificity, efficiency, accuracy and retrieval time were used in developing our heuristic ranking model.

Ranking Model Applying Self-Organizing Maps and Factor Analysis

2021 ◽  
Vol 19 (7) ◽  
pp. 1217-1224
Author(s):  
Daniel Steffen ◽  
Anselmo Chaves Neto
Keyword(s):  
Factor Analysis ◽  
Self Organizing Maps ◽  
Ranking Model ◽  
Self Organizing
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