Personalized news reading via hybrid learning

Ke Chen, Sunny Yeung

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


    In this paper, we present a personalized news reading prototype where latest news articles published by various on-line news providers are automatically collected, categorized and ranked in light of a user's habits or interests. Moreover, our system can adapt itself towards a better performance. In order to develop such an adaptive system, we proposed a hybrid learning strategy; supervised learning is used to create an initial system configuration based on user's feedbacks during registration, while an unsupervised learning scheme gradually updates the configuration by tracing the user's behaviors as the system is being used. Simulation results demonstrate satisfactory performance. © Springer-Verlag Berlin Heidelberg 2004.
    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
    PublisherSpringer Nature
    Number of pages5
    Publication statusPublished - 2004
    EventInternational Conference on Intelligent Data Engineering and Automated Learning -
    Duration: 1 Jan 1824 → …


    ConferenceInternational Conference on Intelligent Data Engineering and Automated Learning
    Period1/01/24 → …


    Dive into the research topics of 'Personalized news reading via hybrid learning'. Together they form a unique fingerprint.

    Cite this