TY  -  
T1  - How Relevance Feedback is Framed Affects User Experience, but not Behaviour 
SN  -  /  
UR  - http://hdl.handle.net/10138/313971 
T3  -  
A1  - Tripathi, Dhruv; Medlar, Alan; Glowacka, Dorota 
A2  -  
PB  - ACM 
Y1  - 2019 
LA  - eng 
AB  - Retrieval systems based on machine learning require both positive and negative examples to perform inference, which is usually obtained through relevance feedback. Unfortunately, explicit negative relevance feedback is thought to have poor user experience. Instead, systems typically rely on implicit negative feedback. In this study, we confirm that, in the case of binary relevance feedback, users prefer giving positive feedback ( and implicit negative feedback) over negative feedback ( and impli... 
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KW  - relevance feedback; negative relevance feedback; user studies; experimental design; scientific literature search; RETRIEVAL; CBIR; 113 Computer and information sciences 
N1  -   
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ER  -