Local Factor Models for Large-Scale Inductive Recommendation
Local Factor Models for Large-Scale Inductive Recommendation
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DOI：
10.1145/3460231.3474276
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Longqi Yang;Tobias Schnabel;Paul N. Bennett;S. Dumais
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文献类型：
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作者：
Longqi Yang;Tobias Schnabel;Paul N. Bennett;S. Dumais
在许多领域中，用户偏好在志同道合的用户子组中是相似的，但通常在这些子组之间存在全局差异。本地推荐模型被证明在这种设置中大大提高了top-K推荐性能。然而，现有的本地模型不能扩展到具有越来越多的子组的大规模数据集，并且不支持针对未出现在训练集中的用户的归纳推荐。其主要原因是子组检测和推荐在模型中作为单独的步骤实现，或者局部模型为每个子组显式实例化。在本文中，我们提出了一个端到端的本地因子模型（Elfm），它克服了这些限制，通过结合这两个步骤，并通过归纳偏见纳入本地结构。我们的模型可以进行端到端的优化，并支持增量推理，不需要为每个子组提供完整的单独模型，并且具有用于合并局部结构的整体小内存和计算成本。实证结果表明，我们的方法大大提高了推荐性能的大规模数据集上的数百万用户和项目与相当小的模型大小。我们的用户研究还表明，我们的方法产生连贯的项目子组，这可能有助于生成可解释的建议。
In many domains, user preferences are similar locally within like-minded subgroups of users, but typically differ globally between those subgroups. Local recommendation models were shown to substantially improve top-K recommendation performance in such settings. However, existing local models do not scale to large-scale datasets with an increasing number of subgroups and do not support inductive recommendations for users not appearing in the training set. Key reasons for this are that subgroup detection and recommendation get implemented as separate steps in the model or that local models are explicitly instantiated for each subgroup. In this paper, we propose an End-to-end Local Factor Model (Elfm) which overcomes these limitations by combining both steps and incorporating local structures through an inductive bias. Our model can be optimized end-to-end and supports incremental inference, does not require a full separate model for each subgroup, and has overall small memory and computational costs for incorporating local structures. Empirical results show that our method substantially improves recommendation performance on large-scale datasets with millions of users and items with considerably smaller model size. Our user study also shows that our approach produces coherent item subgroups which could aid in the generation of explainable recommendations.