Recommender system architecture based on Mahout and a main memory database

In this study, we propose a news recommendation system architecture using a main memory database (DB) and Mahout. The user’s news preference rate is calculated automatically based on the time the user spends reading news items and their length. While existing systems also infer the user’s preferred fields, our system adjusts the volume and ratio of news stories using these categories. We collect web pages accessed by the user on a smart device and classify them using a naive Bayes classifier to determine the user’s preferred news categories. Collaborative filtering is then used to search for related news items read by others and to recommend news in a ratio consistent with the user’s preferred fields. Using a main memory DB, recommendations are computed 2.1 times faster than with a traditional DB when recommending from among 100,000 items; further, the more data used for recommendations, the bigger the speed difference between the proposed and traditional systems becomes.