Proposed Model of Academic Reading Material Recommendation System

Tsarina Dwi Putri, Zulkarnain

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Cold-start problem and cold-item are something that will happen when an early developed online library of educational institution library tries to recommend scientific articles to users. The reading materials do not even have reviews and/or ratings from previous users, no users have expressed preferences yet, also solely rely on keywords in search engines. The fact that there are abundant holdings in the library, it needs to effectively maintain users' interests to borrow and download academic reading material in accordance with users' interest from holdings in the library repository. This study seeks to provide novelty by finding another way to utilize dataset with only using abstract and title variables as an input parallelly that can provide effective results as a recommendation system. It proposes a word embedding model to be used as topic modeling for the content-based recommendation system to overcome the problems, wherein the attributes are minimum (such as title, author, and abstract) and user data are not available.

Original languageEnglish
Title of host publicationAsia Pacific Conference on Research in Industrial and Systems Engineering, APCORISE 2020 - Proceedings
PublisherAssociation for Computing Machinery
Pages105-109
Number of pages5
ISBN (Electronic)9781450376006
DOIs
Publication statusPublished - 16 Jun 2020
Event3rd Asia Pacific Conference on Research in Industrial and Systems Engineering, APCORISE 2020 - Depok, Online, Indonesia
Duration: 16 Jun 2020 → …

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd Asia Pacific Conference on Research in Industrial and Systems Engineering, APCORISE 2020
Country/TerritoryIndonesia
CityDepok, Online
Period16/06/20 → …

Keywords

  • Academic reading material
  • neural network embedding
  • recommendation system
  • word embedding

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