UP-TreeRec: Building dynamic user profiles tree for news recommendation

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Title: UP-TreeRec: Building dynamic user profiles tree for news recommendation
Authors: He, MingWu, XiaofeiZhang, JiulingDong, Ruihai
Permanent link: http://hdl.handle.net/10197/12196
Date: 22-Apr-2019
Online since: 2021-05-25T15:44:15Z
Abstract: Online 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.
Funding Details: Science Foundation Ireland
Funding Details: Insight Research Centre
Type of material: Journal Article
Publisher: IEEE
Journal: China Communications
Volume: 16
Issue: 4
Start page: 219
End page: 233
Copyright (published version): 2019 IEEE
Keywords: Recommender systemsNews recommendationsUser profilingContent-based recommendationCollaborative filtering
DOI: 10.12676/j.cc.2019.04.017
Other versions: https://ieeexplore.ieee.org/document/8695430
Language: en
Status of Item: Peer reviewed
ISSN: 1673-5447
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Insight Research Collection

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