媒体推荐系统中彩票假说的研究
Exploring Lottery Ticket Hypothesis in Media Recommender Systems
摘要：
媒体推荐系统旨在捕捉用户的偏好，并提供精确的个性化推荐的媒体内容。在现代推荐模型的共同范例中有两个关键组件：（1）表示学习，它为每个用户和项目生成嵌入;以及（2）交互建模，它根据项目的表示来拟合用户对项目的偏好。尽管取得了很大的成功，但当存在大量的用户和项目时，通常需要创建、存储和优化一个巨大的嵌入表，其中模型参数的规模很容易达到数百万甚至更大。因此，它自然会对重型推荐模型提出疑问：我们真的需要这么大规模的参数吗？我们从最近提出的彩票假说（LTH）中得到启发，该假说认为密集和过度参数化的模型包含一个更小和更稀疏的子模型，可以达到与完整模型相当的性能。在本文中，我们将LTH扩展到媒体推荐系统，旨在发现深层推荐模型中的中奖门票。据我们所知，这是第一个工作，研究长期的媒体推荐系统。以MF和LightGCN为骨干模型，我们发现在推荐模型中普遍存在中奖票。在Yelp 2018、TikTok和Kwai三个媒体融合数据集上，中奖票分别仅需29%~ 48%、7%~10%和3%~17%的参数就能达到相当的推荐性能。
英文摘要：
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. In spite 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
