Evaluating sources of implicit feedback in web searches

Author(s):  
Xin Fu
Author(s):  
Yu Zhou ◽  
Xinying Yang ◽  
Taolue Chen ◽  
Zhiqiu Huang ◽  
Xiaoxing Ma ◽  
...  
Keyword(s):  

2021 ◽  
Vol 455 ◽  
pp. 59-67
Author(s):  
Yongxin Ni ◽  
Xiancong Chen ◽  
Weike Pan ◽  
Zixiang Chen ◽  
Zhong Ming

2001 ◽  
Vol 1 (3) ◽  
pp. 28-31 ◽  
Author(s):  
Valerie Stevenson

Looking back to 1999, there were a number of search engines which performed equally well. I recommended defining the search strategy very carefully, using Boolean logic and field search techniques, and always running the search in more than one search engine. Numerous articles and Web columns comparing the performance of different search engines came to different conclusions on the ‘best’ search engines. Over the last year, however, all the speakers at conferences and seminars I have attended have recommended Google as their preferred tool for locating all kinds of information on the Web. I confess that I have now abandoned most of my carefully worked out search strategies and comparison tests, and use Google for most of my own Web searches.


2021 ◽  
Vol 3 (2) ◽  
pp. 66-72
Author(s):  
Riad Taufik Lazwardi ◽  
Khoirul Umam

The analysis used in this study uses the help of Google Analytics to understand how the user's behavior on the Calculus learning material educational website page. Are users interested in recommendation articles? The answer to this question provides insight into the user's decision process and suggests how far a click is the result of an informed decision. Based on these results, it is hoped that a strategy to generate feedback from clicks should emerge. To evaluate the extent to which feedback shows relevance, versus implicit feedback to explicit feedback collected manually. The study presented in this study differs in at least two ways from previous work assessing the reliability of implicit feedback. First, this study aims to provide detailed insight into the user decision-making process through the use of a recommendation system with an implicit feedback feature. Second, evaluate the relative preferences that come from user behavior (user behavior). This differs from previous studies which primarily assessed absolute feedback. 


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