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Rokach, L.; Kalech, M.; Blank, I.; Stern, R.: Who is going to win the next Association for the Advancement of Artificial Intelligence Fellowship Award? : evaluating researchers by mining bibliographic data (2011)
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- Abstract
- Accurately evaluating a researcher and the quality of his or her work is an important task when decision makers have to decide on such matters as promotions and awards. Publications and citations play a key role in this task, and many previous studies have proposed using measurements based on them for evaluating researchers. Machine learning techniques as a way of enhancing the evaluating process have been relatively unexplored. We propose using a machine learning approach for evaluating researchers. In particular, the proposed method combines the outputs of three learning techniques (logistics regression, decision trees, and artificial neural networks) to obtain a unified prediction with improved accuracy. We conducted several experiments to evaluate the model's ability to: (a) classify researchers in the field of artificial intelligence as Association for the Advancement of Artificial Intelligence (AAAI) fellows and (b) predict the next AAAI fellowship winners. We show that both our classification and prediction methods are more accurate than are previous measurement methods, and reach a precision rate of 96% and a recall of 92%.
- Source
- Journal of the American Society for Information Science and Technology. 62(2011) no.12, S.2456-2470
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Shani, G.; Rokach, L.; Shapira, B.; Hadash, S.; Tangi, M.: Investigating confidence displays for top-N recommendations (2013)
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- Abstract
- Recommendation systems often compute fixed-length lists of recommended items to users. Forcing the system to predict a fixed-length list for each user may result in different confidence levels for the computed recommendations. Reporting the system's confidence in its predictions (the recommendation strength) can provide valuable information to users in making their decisions. In this article, we investigate several different displays of a system's confidence to users and conclude that some displays are easier to understand and are favored by most users. We continue to investigate the effect confidence has on users in terms of their perception of the recommendation quality and the user experience with the system. Our studies show that it is not easier for users to identify relevant items when confidence is displayed. Still, users appreciate the displays and trust them when the relevance of items is difficult to establish.
- Source
- Journal of the American Society for Information Science and Technology. 64(2013) no.12, S.2548-2563
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Rokach, L.; Mitra, P.: Parsimonious citer-based measures : the artificial intelligence domain as a case study (2013)
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- Source
- Journal of the American Society for Information Science and Technology. 64(2013) no.9, S.1951-1959
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Ofek, N.; Rokach, L.: ¬A classifier to determine which Wikipedia biographies will be accepted (2015)
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- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.1, S.213-218
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Blank, I.; Rokach, L.; Shani, G.: Leveraging metadata to recommend keywords for academic papers (2016)
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- Source
- Journal of the Association for Information Science and Technology. 67(2016) no.12, S.3073-3091
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Greenstein-Messica, A.; Rokach, L.; Shabtai, A.: Personal-discount sensitivity prediction for mobile coupon conversion optimization (2017)
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- Source
- Journal of the Association for Information Science and Technology. 68(2017) no.8, S.1940-1952
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Greenstein-Messica, A.; Rokach, L.; Shabtai, A.: Personal-discount sensitivity prediction for mobile coupon conversion optimization (2017)
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- Source
- Journal of the Association for Information Science and Technology. 68(2017) no.8, S.1940-1952
