Description

Recommending online news articles has become a promising research direction as the Internet provides fast access to real-time information from multiple sources around the world. Many online readers have their own reading preference on news articles; however, a group of users might be interested in similar fascinating topics. It would be helpful to take into consideration the individual and group reading behavior simultaneously when recommending news items to online users. In this paper, we propose PENETRATE, a novel PErsonalized NEws recommendaTion framework using ensemble hieRArchical clusTEring to provide attractive recommendation results. Specifically, given a set of online readers, our approach initially separates readers into different groups based on their reading histories, where each user might be designated to several groups. Once a collection of newly-published news items is provided, we can easily construct a news hierarchy for each user group. When recommending news articles to a given user, the hierarchies of multiple user groups that the user belongs to are merged into an optimal one. Finally a list of news articles are selected from this optimal hierarchy based on the user’s personalized information, as the recommendation result. Extensive empirical experiments on a set of news articles collected from various popular news websites demonstrate the efficacy of our proposed approach.