News Recommender System Based on User Log History Using Rapid Automatic Keyword Extraction

Abstract

There are many ways to find information; one of them is reading online news. However, searching for news online becomes more difficult because we should visit multiple platforms to find information. Sometimes, the recommended news doesn't match the user's interests. In many prior works, news recommendations are based on trending. Thus, the recommended news may not necessarily match the user's interests. To overcome this, we built a web-based news recommender system to make it easier for users to find news. We use the Rapid Automatic Keyword Extraction (RAKE) method in the recommendation process because this method can recommend news based on user preferences by utilizing user history logs. RAKE converts the title and content of the news into vector representation using Count vectorizer and applies the Cosine Similarity function to compare similarities between news. The test results show that the average performance of our proposed system is 90.8%, this accuracy outperforms earlier systems in terms of performance by the purpose of the recommender system, i.e., diversity, novelty, and relevance.