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  • Enhancing Collaborative Filtering with Generative Augmentation

    Author(s)
    Wang, Qinyong
    Yin, Hongzhi
    Wang, Hao
    Nguyen, Quoc Viet Hung
    Huang, Zi
    Cui, Lizhen
    Griffith University Author(s)
    Nguyen, Henry
    Year published
    2019
    Metadata
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    Abstract
    Collaborative filtering (CF) has become one of the most popular and widely used methods in recommender systems, but its performance degrades sharply for users with rare interaction data. Most existing hybrid CF methods try to incorporate side information such as review texts to alleviate the data sparsity problem. However, the process of exploiting and integrating side information is computationally expensive. Existing hybrid recommendation methods treat each user equally and ignore that the pure CF methods have already achieved both effective and efficient recommendation performance for active users with sufficient interaction ...
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    Collaborative filtering (CF) has become one of the most popular and widely used methods in recommender systems, but its performance degrades sharply for users with rare interaction data. Most existing hybrid CF methods try to incorporate side information such as review texts to alleviate the data sparsity problem. However, the process of exploiting and integrating side information is computationally expensive. Existing hybrid recommendation methods treat each user equally and ignore that the pure CF methods have already achieved both effective and efficient recommendation performance for active users with sufficient interaction records and the little improvement brought by side information to these active users is ignorable. Therefore, they are not cost-effective solutions. One cost-effective idea to bypass this dilemma is to generate sufficient "real" interaction data for the inactive users with the help of side information, and then a pure CF method could be performed on this augmented dataset effectively. However, there are three major challenges to implement this idea. Firstly, how to ensure the correctness of the generated interaction data. Secondly, how to combine the data augmentation process and recommendation process into a unified model and train the model end-to-end. Thirdly, how to make the solution generalizable for various side information and recommendation tasks. In light of these challenges, we propose a generic and effective CF model called AugCF that supports a wide variety of recommendation tasks. AugCF is based on Conditional Generative Adversarial Nets that additionally consider the class (like or dislike) as a feature to generate new interaction data, which can be a sufficiently real augmentation to the original dataset. Also, AugCF adopts a novel discriminator loss and Gumbel-Softmax approximation to enable end-to-end training. Finally, extensive experiments are conducted on two large-scale recommendation datasets, and the experimental results show the superiority of our proposed model.
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    Conference Title
    KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
    DOI
    https://doi.org/10.1145/3292500.3330873
    Subject
    Artificial intelligence
    Science & Technology
    Computer Science, Information Systems
    Computer Science, Theory & Methods
    Publication URI
    http://hdl.handle.net/10072/392470
    Collection
    • Conference outputs

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