@article{(Open Science Index):https://publications.waset.org/pdf/10013446,
	  title     = {Impact of Similarity Ratings on Human Judgement},
	  author    = {Ian A. McCulloh and  Madelaine Zinser and  Jesse Patsolic and  Michael Ramos},
	  country	= {},
	  institution	= {},
	  abstract     = {Recommender systems are a common artificial intelligence (AI) application. For any given input, a search system will return a rank-ordered list of similar items. As users review returned items, they must decide when to halt the search and either revise search terms or conclude their requirement is novel with no similar items in the database. We present a statistically designed experiment that investigates the impact of similarity ratings on human judgement to conclude a search item is novel and halt the search. In the study, 450 participants were recruited from Amazon Mechanical Turk to render judgement across 12 decision tasks. We find the inclusion of ratings increases the human perception that items are novel. Percent similarity increases novelty discernment when compared with star-rated similarity or the absence of a rating. Ratings reduce the time to decide and improve decision confidence. This suggests that the inclusion of similarity ratings can aid human decision-makers in knowledge search tasks.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {18},
	  number    = {1},
	  year      = {2024},
	  pages     = {28 - 32},
	  ee        = {https://publications.waset.org/pdf/10013446},
	  url   	= {https://publications.waset.org/vol/205},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 205, 2024},
	}