Causal Combinatorial Factorization Machines for Set-wise Recommendation
Causal Combinatorial Factorization Machines for Set-wise Recommendation
复制标题
用于集合推荐的因果组合分解机
DOI：
10.1007/978-3-030-75765-6_40
复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
Hisashi Kashima
中科院分区：
文献类型：
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作者：
Akira Tanimoto;Tomoya Sakai;Takashi Takenouchi;Hisashi Kashima
与集明智的（exact-k，石板，组合）推荐，我们的目标是优化整个项目的建议，同时考虑项目之间的依赖关系。这使得我们能够对例如项目的替代关系进行建模，即，与其中假设项目独立性的top-k推荐相反，顾客倾向于仅购买同一类别中的一个项目。最近在这方面的努力集中在计算方面的优化项目的建议。然而，他们没有考虑到数据集中的样本选择偏差。用于推荐的真实世界数据集由于有偏见的曝光或用户偏好而具有不完全随机的缺失条目。解决选择偏差对于集合式推荐很重要，因为具有较大假设空间的方法更有可能过拟合有偏差的训练数据。鉴于最近的top-krecommendation的研究，已经解决了这个问题，通过使用因果推理技术，因此，我们提出了一个集明智的推荐模型与去偏训练方法的基础上，最近的因果推理技术。我们证明了我们的方法使用真实世界的推荐数据集组成的偏见训练集和随机测试集的优势。
With set-wise (exact-k, slate, combinatorial) recommendation, we aim to optimize the whole set of items to recommend while taking the dependency among items into consideration. This enables us to model, for example, the substitution relationship of items, i.e., a customer tends to purchase only one item in the same category, in contrast to the top-krecommendation in which the independency of items is assumed. Recent efforts in this context have focused on the computational aspects of optimizing the set of items to recommend. However, they have not taken into account sample selection bias in datasets. Real-world datasets for recommendation have missing entries not completely at random due to biased exposure or user preferences. Addressing the selection bias is important for the set-wise recommendation since methods with larger hypothesis spaces are more likely to overfit biased training data. In light of recent top-krecommendation research that has addressed this issue by using causal inference techniques, we therefore propose a set-wise recommendation model with debiased training methods based on recent causal inference techniques. We demonstrate the advantage of our method using real-world recommendation datasets consisting of biased training sets and randomized test sets.