Incorporating News Tags into Neural News Recommendation in Indonesian Language

News recommendation system holds the potential to aid users in discovering articles that align with their interests, which is critical to alleviate user information overload. To generate effective news recommendations, one key capability is to accurately capture the contextual meaning of text in the news articles, since this is pivotal in acquiring useful representations for both news content and users. In this work, we examine the effectiveness of neural news recommendation with attentive multi-view learning (NAML) method to conduct a news recommendation task in the Indonesian language. We further propose to incorporate news tags, which at some levels may capture the important contextual meanings contained in the news articles, to improve the effectiveness of the NAML method in the Indonesian news recommendation system. Our results show that the NAML method leads to significant improvement (if not comparable) in the effectiveness of neural-based Indonesian news recommendations. Further incorporating news tags is shown to significantly increase the performance of the NAML method by 5.86% in terms of NDCG@5 metric.