Paper Recommender System Using Big Data Tools
To face the problem of information overload, digital libraries, like other businesses, have used recommender systems and try to personalize recommendations to users by using the textual information of papers. This textual information includes title, abstract, keywords, publisher, author and other si...
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| Main Authors: | , , , |
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| Format: | Book Chapter |
| Language: | English |
| Published: |
IntechOpen
2022
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| Subjects: | |
| Online Access: | https://www.intechopen.com/chapters/85391 |
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| Summary: | To face the problem of information overload, digital libraries, like other businesses, have used recommender systems and try to personalize recommendations to users by using the textual information of papers. This textual information includes title, abstract, keywords, publisher, author and other similar items. Since the volume of papers is increasing day by day and recommender systems do not have the ability to cover this huge volume to process papers according to the user’s tastes, that is why we need to use our papers to cover and process this volume quickly. We have big data tools, which will offer relevant recommendations by running parallel processing. In this chapter, the researches and researches of researchers in the field of recommender systems/aware of the text of scientific papers and recommender systems have been discussed. |
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| ISBN: | 978-1-83769-180-7 |
| DOI: | 10.5772/intechopen.109136 |