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    Generative Adversarial Zero-Shot Learning For Cold-start News Recommendation

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    Name:
    Manal Alshehri_Generative Adversarial Zero-Shot Learning For Cold-start News Recommendation.pdf
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    2.423Mb
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    Type
    Poster
    Authors
    Alshehri, Manal
    Date
    2022-11-15
    Permanent link to this record
    http://hdl.handle.net/10754/685692
    
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    Abstract
    News recommendation models extremely rely on the interactive information between users and news articles to personalize the recommendation. Therefore, one of their most serious challenges is the cold-start problem (CSP). Their performance is dropped intensely for new users or new news. Zero-shot learning helps in synthesizing a virtual representation of the missing data in a variety of application tasks. Therefore, it can be a promising solution for CSP to generate virtual interaction behaviors for new users or new news articles. In this work, we utilize the generative adversarial zero-shot learning in building a framework, namely, GAZRec, which is able to address the CSP caused by purely new users or new news. GAZRec can be flexibly applied to any neural news recommendation model. According to the experimental evaluations, applying the proposed framework to various news recommendation baselines attains a significant AUC improvement of 1% - 21% in different cold start scenarios and 1.2% - 6.6% in the regular situation when both users and news have a few interactions.
    Conference/Event name
    KAUST Research Conference SCML2026
    Collections
    KAUST Research Conference SCML2022; Posters

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