Book recommended formulation based on multiple bibliographic information

Maneewongvatana, Suthathip; Suntornacane, A.; Assawawayuyothin, N.

doi:10.1145/3290511.3290548

Cited by 1 publication

(3 citation statements)

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“…Social Recommender system generates the recommendation based on users social information like social tags, trust ratings used on social networking sites [29]. Aspect Based Sentiment Analysis [30] generates the recommendations based on sentiments. Few researchers try to recommend the books to the user based on their age and gender which is identified through real time video, while the reader is entering in the library [31].…”

Section: Association Rule Miningmentioning

confidence: 99%

“…Precision: It describes the ratio of correctly recommended books and total recommendation. It indicates how many recommendations were useful to the user [7], [12], [14], [16], [22], [26], [30], [35].…”

Section: Evaluation Techniques For Recommender Systemsmentioning

confidence: 99%

“…Recall: It measures the presence of preferred and relevant items in the sequence of recommended items. It is defined as the ratio of correctly recommended books to the relevant books [4], [7], [12], [14], [16], [22], [25], [26], [30], [35].…”

Section: Evaluation Techniques For Recommender Systemsmentioning

confidence: 99%

See 2 more Smart Citations

Framework for Hybrid Book Recommender System based on Opinion Mining

Kumar*,

Chawla

2019

IJRTE

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Recommender system plays an important role in automatic filtering out the important and personalized information for the intended user from a large amount of available information on internet. Recommender systems for books provide personalized recommendations to the readers for reading and to the librarians for book acquisition process. The objective of this research paper is four folds. Firstly, it conducts an extensive literature review pertaining to book recommender systems, secondly it specifies the popular recommendation techniques being used in specific application area of books, thirdly the paper reflects on the methodology followed and evaluation techniques being used based on the techniques discussed. Lastly, the paper proposes a framework for a book recommender system using best-suited recommendation techniques.

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Section: Association Rule Miningmentioning

confidence: 99%