The PDF file you selected should load here if your Web browser has a PDF reader plug-in installed (for example, a recent version of Adobe Acrobat Reader).
If you would like more information about how to print, save, and work with PDFs, Highwire Press provides a helpful Frequently Asked Questions about PDFs.
Alternatively, you can download the PDF file directly to your computer, from where it can be opened using a PDF reader. To download the PDF, click the Download link above.
BibTex Citation Data :
@article{JOIV678,
author = {Budi Juarto and Abba Suganda Girsang},
title = {Neural Collaborative with Sentence BERT for News Recommender System},
journal = {JOIV : International Journal on Informatics Visualization},
volume = {5},
number = {4},
year = {2021},
keywords = {Recommender system; news; neural networks; sentence BERT, neural collaborative filtering.},
abstract = {The number of news produced every day is as much as 3 million per day, making readers have many choices in choosing news according to each reader's topic and category preferences. The recommendation system can make it easier for users to choose the news to read. The method that can be used in providing recommendations from the same user is collaborative filtering. Neural collaborative filtering is usually being used for recommendation systems by combining collaborative filtering with neural networks. However, this method has the disadvantage of recommending the similarity of news content such as news titles and content to users. This research wants to develop neural collaborative filtering using sentences BERT. Sentence BERT is applied to news titles and news contents that are converted into sentence embedding. The results of this sentence embedding are used in neural collaboration with item id, user id, and news category. We use a Microsoft news dataset of 50,000 users and 51,282 news, with 5,475,542 interactions between users and news. The evaluation carried out in this study uses precision, recall, and ROC curves to predict news clicks by the user. Another evaluation uses a hit ratio with the leave one out method. The evaluation results obtained a precision value of 99.14%, recall of 92.48%, f1-score of 95.69%, and ROC score of 98%. Evaluation measurement using the hit ratio@10 produces a hit ratio of 74% at fiftieth epochs for neural collaborative with sentence BERT which is better than neural collaborative filtering (NCF) and NCF with news category.},
issn = {2549-9904}, pages = {448--455}, doi = {10.30630/joiv.5.4.678},
url = {https://joiv.org/index.php/joiv/article/view/678}
}
Refworks Citation Data :
@article{{JOIV}{678}, author = {Juarto, B., Suganda Girsang, A.}, title = {Neural Collaborative with Sentence BERT for News Recommender System}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {5}, number = {4}, year = {2021}, doi = {10.30630/joiv.5.4.678}, url = {} }Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
__________________________________________________________________________
JOIV : International Journal on Informatics Visualization
ISSN 2549-9610 (print) | 2549-9904 (online)
Organized by Department of Information Technology - Politeknik Negeri Padang, and Institute of Visual Informatics - UKM and Soft Computing and Data Mining Centre - UTHM
W : http://joiv.org
E : joiv@pnp.ac.id, hidra@pnp.ac.id, rahmat@pnp.ac.id
View JOIV Stats
is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.