He, MingMingHeWu, XiaofeiXiaofeiWuZhang, JiulingJiulingZhangDong, RuihaiRuihaiDong2021-05-252021-05-252019 IEEE2019-04-22China Communications1673-5447http://hdl.handle.net/10197/12196Online News recommendation systemsaim to address the information explosion of news and make personalized recommendation for users. The key problem of personalized news recommendation is to model users’ interests and track their changes. A common way to deal with usermodeling problem is to build user profiles from observed behavior. However, the majority of existing methods make static representation to user profiles and little research has focused on the effective user modeling that could dynamically capture user interests in news topics.To address this problem, in this paper, we propose UP-TreeRec, a news recommendation framework based on user profile tree (UP-Tree), which is a novel framework combining content-based and collaborative filtering techniques. First, by exploitinga novel topic model namely UI-LDA, we obtain the representationvectorsfor news content in topic spaceas the fundamentalbridgeto associate user interests with news topics. Next, we design a decision tree with a dynamically changeable structure to construct a user interest profile from his feedback. Furthermore, we present a clustering based multidimensional similarity computation method to select the nearest neighbor of UP-Treeefficiently. We also provide a Map-Reduce framework based implementation that enables scaling our solution to real-world news recommendation problems.We conducted several experiments compared to the state-of-the-art approaches on real-world datasets and the experimental results demonstrate that our approach significantly improves the accuracy and effectiveness in news recommendation.en© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Recommender systemsNews recommendationsUser profilingContent-based recommendationCollaborative filteringJournal Article16421923310.12676/j.cc.2019.04.0172020-06-16https://creativecommons.org/licenses/by-nc-nd/3.0/ie/