Timeline for Precision and recall in content-based recommender
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Sep 11 at 19:03 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
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Sep 7, 2023 at 20:13 | comment | added | Nuclear Hoagie | You haven't described a practical measure of performance for this data. You give the user a list of recommended articles, but don't describe any means of determining whether those articles are actually useful to the user or not. It seems most of the data would be unlabeled here, for which there is no concept of accuracy, precision, or recall - you can't know what proportion of the relevant items you found without knowing the relevance of every individual item. | |
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Mar 25, 2022 at 10:27 | answer | added | Tim | timeline score: 0 | |
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Nov 24, 2021 at 7:00 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
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Nov 17, 2020 at 6:02 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
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Nov 8, 2019 at 16:02 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
Oct 8, 2019 at 19:14 | answer | added | Sebastian | timeline score: 0 | |
Oct 8, 2019 at 19:01 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
Jun 7, 2019 at 9:03 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
Feb 6, 2019 at 6:01 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
Jun 17, 2016 at 0:00 | answer | added | Help_please | timeline score: 0 | |
Jun 16, 2016 at 23:20 | comment | added | jeff | I see. then the number of false negatives should be the ones you returned but not in the user's library, which is not necessarily 0. right? | |
Jun 16, 2016 at 22:24 | comment | added | Roy C | @HalilPazarlama I guess a more appropriate definition for precision and recall for recommender systems would be, precision = (# of recommendations in the user's library)/(total number of recommendations), recall = (# of recommendations in the user's library)/(# of articles in the library). | |
Jun 16, 2016 at 22:18 | comment | added | jeff | It depends on your definition of true positive (i.e. a successful guess), hence your performance measure. this and this might be related. | |
Jun 16, 2016 at 21:06 | history | asked | Roy C | CC BY-SA 3.0 |