Article Feed Recommendation System for Thai Social Media Application Using Article Context Based on Deep Learning
Authors: 
Pannawit Athipatcharawat, Dhata Muangrux, Ekapol Chuangsuwanich, Pittipol KantavatAuthors Info & Claims
Pannawit Athipatcharawat, Dhata Muangrux, Ekapol Chuangsuwanich, Pittipol KantavatAuthors Info & ClaimsArticle No.: 12, Pages 1 - 7
Abstract
In recent years, social media applications have exhibited significant growth in the number of users. Enhancing content alignment with user preferences is essential in developing a sophisticated recommendation system. Neural networks have become pivotal in improving the performance of recommendation systems. However, the utilization of auxiliary information and text data remains markedly underexplored, especially in the context of Thai recommendation systems. Therefore, this study aims to bridge this gap by developing a recommendation system tailored to Thai social media applications. We focus on leveraging supplementary information and analyzing text features to gain deeper insights into user preferences while addressing the complexity and computational time challenges of handling large social media datasets. We propose utilizing article content through Contextualized Word Embedding (Multilingual Universal Sentence Encoder) and Principal Component Analysis (PCA) within the Deep and Cross Network framework, called ’DCN with MUSE & PCA.’ Our experiments, conducted on a real-world Thai social media application dataset, indicate that the proposed model outperforms the baseline model in terms of performance.
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- Article Feed Recommendation System for Thai Social Media Application Using Article Context Based on Deep Learning
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Published In
December 2023
303 pages
ISBN:9798400708497
DOI:10.1145/3628454
Copyright © 2023 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
New York, NY, United States
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Published: 06 December 2023
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IAIT 2023
IAIT 2023: 13th International Conference on Advances in Information Technology
December 6 - 9, 2023
Bangkok, Thailand
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