Reusing Search Data in Ranking and Selection

2018 ◽  
Vol 28 (3) ◽  
pp. 1-15 ◽  
Author(s):  
David J. Eckman ◽  
Shane G. Henderson
Keyword(s):  
Ranking And Selection ◽  
Search Data

Using Big Search Data to Map Your Market: Marketing in a Digital Age

IESE Insight ◽  
2017 ◽  
pp. 31-37 ◽  
Author(s):  
Berndt Skiera ◽  
Daniel M. Ringel
Keyword(s):  
Digital Age ◽  
Search Data

Pemanfaatan Sistem Informasi Terhadap Kinerja Pelayanan, Sirkulasi Buku Dan Pengolahan Arsip

2018 ◽  
Vol 4 (1) ◽  
pp. 87-96
Author(s):  
Yanni Suherman
Keyword(s):  
Information System ◽  
Data Processing ◽  
System Development ◽  
Processing System ◽  
Data Processing System ◽  
Data Archiving ◽  
Data Archives ◽  
Development Life Cycle ◽  
Search Data

Research conducted at the Office of Archives and Library of Padang Pariaman Regency aims to find out the data processing system library and data archiving. All data processing is done is still very manual by using the document in writing and there is also a stacking of archives on the service. By utilizing library information systems and archives that will be applied to the Office of Archives and Library of Padang Pariaman Regency can improve the quality of service that has not been optimal. This research was made by using System Development Life Cycle (SDLC) which is better known as waterfall method. The first step taken on this method is to go directly to the field by conducting interviews and discussions. This information system will be able to assist the work of officers in terms of data processing libraries and facilitate in search data archives by presenting reports more accurate, effective and efficient.

Unbiased Learning to Rank

2021 ◽  
Vol 39 (2) ◽  
pp. 1-29
Author(s):  
Qingyao Ai ◽  
Tao Yang ◽  
Huazheng Wang ◽  
Jiaxin Mao
Keyword(s):  
Online Learning ◽  
Theoretical Foundation ◽  
Learning To Rank ◽  
Research Question ◽  
Parameters Estimation ◽  
User Interactions ◽  
Empirical Performance ◽  
Search Data ◽  
Two Sides ◽  
Important Research Question

How to obtain an unbiased ranking model by learning to rank with biased user feedback is an important research question for IR. Existing work on unbiased learning to rank (ULTR) can be broadly categorized into two groups—the studies on unbiased learning algorithms with logged data, namely, the offline unbiased learning, and the studies on unbiased parameters estimation with real-time user interactions, namely, the online learning to rank. While their definitions of unbiasness are different, these two types of ULTR algorithms share the same goal—to find the best models that rank documents based on their intrinsic relevance or utility. However, most studies on offline and online unbiased learning to rank are carried in parallel without detailed comparisons on their background theories and empirical performance. In this article, we formalize the task of unbiased learning to rank and show that existing algorithms for offline unbiased learning and online learning to rank are just the two sides of the same coin. We evaluate eight state-of-the-art ULTR algorithms and find that many of them can be used in both offline settings and online environments with or without minor modifications. Further, we analyze how different offline and online learning paradigms would affect the theoretical foundation and empirical effectiveness of each algorithm on both synthetic and real search data. Our findings provide important insights and guidelines for choosing and deploying ULTR algorithms in practice.

scholarly journals Tracking U.S. Pertussis Incidence: Correlation of Public Health Surveillance and Google Search Data Varies by State

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Christopher H. Arehart ◽  
Michael Z. David ◽  
Vanja Dukic
Keyword(s):  
Public Health ◽  
Linear Models ◽  
State Level ◽  
Individual State ◽  
Control And Prevention ◽  
Search Data ◽  
State Models ◽  
Uniform Manner ◽  
Google Search ◽  
The U.S

AbstractThe Morbidity and Mortality Weekly Reports of the U.S. Centers for Disease Control and Prevention document a raw proxy for counts of pertussis cases in the U.S., and the Project Tycho (PT) database provides an improved source of these weekly data. These data are limited because of reporting delays, variation in state-level surveillance practices, and changes over time in diagnosis methods. We aim to assess whether Google Trends (GT) search data track pertussis incidence relative to PT data and if sociodemographic characteristics explain some variation in the accuracy of state-level models. GT and PT data were used to construct auto-correlation corrected linear models for pertussis incidence in 2004–2011 for the entire U.S. and each individual state. The national model resulted in a moderate correlation (adjusted R2 = 0.2369, p < 0.05), and state models tracked PT data for some but not all states. Sociodemographic variables explained approximately 30% of the variation in performance of individual state-level models. The significant correlation between GT models and public health data suggests that GT is a potentially useful pertussis surveillance tool. However, the variable accuracy of this tool by state suggests GT surveillance cannot be applied in a uniform manner across geographic sub-regions.

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