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 by the Mean Average Precision @K (MAP@K) metric.
