Abstract Recommender systems have been an important tool to filter and tailor the best content for online users. Classical recommender system methods typically face the filter bubble problem where users effectively get isolated from a diversity of viewpoints or content. How to provide relevant and diversified goods for online users has become a challenging problem. In this study, we develop a cross-domain matrix factorization model based on adaptive diversity regularization to address the above challenges. We leverage collective MF model to transfer users’ rating pattern, utilize social tags to transfer semantic information between domains, and design a novel adaptive diversity regularization to improve recommendation performance. Comprehensive experiments on real cross-domain datasets demonstrate the effectiveness of our model. Results show that our model can achieve a decent balance between recommendation accuracy and diversity, and the recommendation polarity can also be alleviated.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Prick the filter bubble: A novel cross domain recommendation model with adaptive diversity regularization


    Contributors:
    Sun, Jianshan (author) / Song, Jian (author) / Jiang, Yuanchun (author) / Liu, Yezheng (author) / Li, Jun (author)

    Published in:

    Publication date :

    2021




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    BKL:    83.72 Verkehrswirtschaft / 85.40 Marketing



    Novel anti-prick tire

    SHEN WENMING | European Patent Office | 2016

    Free access

    A review on cross domain recommendation

    Bansal, Aman / Kumar, Shubham / Yadav, Rahul et al. | IEEE | 2017


    The Usefulness of PRICK Tests in Patients with Atopic Dermatitis

    Samochocki, Z. / Zabielski, S. / Paluchowska, E. et al. | British Library Conference Proceedings | 2002


    Rubber composition for treads of prick resistant loading radial tires

    ZHANG JIANXUN / HUANG YIGANG / GAO YANG et al. | European Patent Office | 2015

    Free access

    Novel View Synthesis Using Locally Adaptive Depth Regularization

    Shah, H. / Chaudhuri, S. | British Library Conference Proceedings | 2006