Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing 2019
DOI: 10.1145/3297280.3297382
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Study of linguistic features incorporated in a literary book recommender system

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Cited by 11 publications
(5 citation statements)
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“…From the results, it can be observed that all the features are collectively playing a pivotal role in author identification than the selected features using correlation. In addition to that, the features identified by us are significant in producing better results than the state-of-art results in the literature [23]. It can be observed from the results that augmentation of each module to the CBF model has improved the recommendation results.…”
Section: Stylometry Feature Extraction Modulementioning
confidence: 62%
“…From the results, it can be observed that all the features are collectively playing a pivotal role in author identification than the selected features using correlation. In addition to that, the features identified by us are significant in producing better results than the state-of-art results in the literature [23]. It can be observed from the results that augmentation of each module to the CBF model has improved the recommendation results.…”
Section: Stylometry Feature Extraction Modulementioning
confidence: 62%
“…We decided to work with recall@k as an evaluation metric. Recall@k shows the number of user's liked test items that were included in the top k of the predicted rankings, divided by k. Similar to Alharthi and Inkpen (2019), we chose k to be 10. We preferred recall@k than precision@k, because if the user has rated less than 10 books, precision@10 for their prediction cannot be 1.…”
Section: Methodsmentioning
confidence: 99%
“…The 21st century is witnessing a major shift in the way people interact with technology and natural language processing (NLP) is playing a central role. A plethora of NLP systems such as question-answering systems (Bouziane et al, 2015;Gillard et al, 2006;Yang et al, 2018) used in diverse fields like health care (Sarrouti and Ouatik El Alaoui, 2017;Zweigenbaum, 2009), education (Godea and Nielsen, 2018;Raamadhurai et al, 2019), privacy (Ravichander et al, 2019;Shvartzshanider et al, 2018); machine translation systems (Cherry et al, 2019;ws-, 2018;Barrault et al, 2019;Nakazawa et al, 2019), conversational agents (Pietquin et al, 2020;Serban et al, 2018;Liu et al, 2016), recommendation systems (Alharthi and Inkpen, 2019;Greenquist et al, 2019) etc are deployed and used by millions of users. NLP systems have become pervasive in current human lifestyle by performing mundane tasks like setting reminders and alarms to complex tasks like replying to emails, booking tickets and recommending movies/restaurants.…”
Section: Introductionmentioning
confidence: 99%