Media recommender systems aim to capture users' preferences and provide precise personalized recommendation of media content. There are two critical components in the common paradigm of modern recommender models: (1) representation learning, which generates an embedding for each user and item; and (2) interaction modeling, which fits user preferences towards items based on their representations. Despite of great success, when a great amount of users and items exist, it usually needs to create, store, and optimize a huge embedding table, where the scale of model parameters easily reach millions or even larger. Hence, it naturally raises questions about the heavy recommender models: Do we really need such large-scale parameters? We get inspirations from the recently proposed lottery ticket hypothesis (LTH), which argues that the dense and over-parameterized model contains a much smaller and sparser sub-model that can reach comparable performance to the full model. In this paper, we extend LTH to media recommender systems, aiming to find the winning tickets in deep recommender models. To the best of our knowledge, this is the first work to study LTH in media recommender systems. With MF and LightGCN as the backbone models, we found that there widely exist winning tickets in recommender models. On three media convergence datasets -- Yelp2018, TikTok and Kwai, the winning tickets can achieve comparable recommendation performance with only 29%~48%, 7%~10% and 3%~17% of parameters, respectively.
翻译:媒体推荐者系统旨在捕捉用户的偏好,并针对媒体内容提供精确的个人化建议。现代推荐者模式的共同范例中有两个关键组成部分:(1) 代表学习,这为每个用户和项目创造了嵌入器;(2) 互动模型,这符合用户对基于其表述的项目的偏好。尽管取得了巨大成功,当大量用户和项目存在时,它通常需要创建、储存和优化一个巨大的嵌入表,模型参数的规模很容易达到数百万甚至更大。因此,它自然地提出了关于重度推荐者模式的疑问:我们真的需要这样的大型参数吗?我们从最近提议的彩票假设中得到灵感,该假设认为密集和超度参数化模型包含一个小得多、稀疏的子模型,能够达到与完整模型相似的性能。在本文中,我们向媒体推荐者系统推广LTH,目的是在深层推荐者模型中找到中奖的门票。根据我们所知的七点,这是在媒体推荐者系统中研究LTH的首项工作。以MF和LightGCN为主基底模型,我们发现三张为基底座组合。我们发现三张得奖的MVIik和KVIVA建议。