Study of linguistic features incorporated in a literary book recommender system

Alharthi, Haifa; Inkpen, Diana

doi:10.1145/3297280.3297382

Cited by 11 publications

(5 citation statements)

References 24 publications

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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%

An Aggrandized Framework for Enriching Book Recommendation System

Sariki

Kumar

2022

MJCS

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In this era of information overload, Recommender Systems have become increasingly important to assist internet users in finding the right choice from umpteen numbers of choices. Especially, in the case of book recommender systems, suggesting an appropriate book by considering user preferences can increase the number of book readers in turn having an aftereffect on the users’ lifestyle by reducing stress, stimulating imagination, improving vocabulary, and making readers smarter. The majority of book recommender systems in the literature have used Collaborative Filtering (CF) and Content-Based Filtering (CBF) methods. Even though CBF methods have shown better performance than CF methods, they are mostly confined to shallow linguistic features. The present work proposed an aggrandized framework having three concurrent modules to improve the recommendation process. NER module extracts the Named Entities from the entire book content which are the key semantic units in providing clues on the possible choices of reading other related books. The Visual feature extraction module analyzes the book front cover to detect objects and text on the cover as well as the description of the cover which can bestow a clue for the genre of that book. The Stylometry module enhances the feature set used in the literature to analyze the author’s literary style for identifying similar authors to the present author of the book. These three modules conjointly improved the overall recommendation accuracy by 18% over the baseline CBF method that indicates the effectiveness of the present framework.

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Section: Stylometry Feature Extraction Modulementioning

confidence: 62%

An Aggrandized Framework for Enriching Book Recommendation System

Sariki

Kumar

2022

MJCS

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“…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%

Overview on NLP Techniques for Content-Based Recommender Systems for Books

Berbatova¹

2019

Proceedings of the Student Research Workshop Associated With RANLP 2019

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Recommender systems are an essential part of today's largest websites. Without them, it would be hard for users to find the right products and content. One of the most popular methods for recommendations is contentbased filtering. It relies on analysing product metadata, a great part of which is textual data. Despite their frequent use, there is still no standard procedure for developing and evaluating content-based recommenders. In this paper, we first examine current approaches for designing, training and evaluating recommender systems based on textual data for books recommendations for the GoodReads website. We examine critically existing methods and suggest how natural language techniques could be employed for the improvement of content-based recommenders.

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“…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%

Case Study: Deontological Ethics in NLP

Prabhumoye

Boldt²,

Salakhutdinov

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Recent work in natural language processing (NLP) has focused on ethical challenges such as understanding and mitigating bias in data and algorithms; identifying objectionable content like hate speech, stereotypes and offensive language; and building frameworks for better system design and data handling practices. However, there has been little discussion about the ethical foundations that underlie these efforts. In this work, we study one ethical theory, namely deontological ethics, from the perspective of NLP. In particular, we focus on the generalization principle and the respect for autonomy through informed consent. We provide four case studies to demonstrate how these principles can be used with NLP systems. We also recommend directions to avoid the ethical issues in these systems.

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Study of linguistic features incorporated in a literary book recommender system

Cited by 11 publications

References 24 publications

An Aggrandized Framework for Enriching Book Recommendation System

An Aggrandized Framework for Enriching Book Recommendation System

Overview on NLP Techniques for Content-Based Recommender Systems for Books

Case Study: Deontological Ethics in NLP

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