基于潜在特征对数线性模型的二元预测
Dyadic Prediction Using a Latent Feature Log-Linear Model
摘要：
在二元预测中，标签必须为成员拥有唯一标识符的对（二元）预测，有时还需要额外的特征，称为边信息。这个问题的特殊情况包括协同过滤和链接预测。我们提出了满足几个重要要求的二元预测的第一个模型：（i）标签可以是有序的或标称的，（ii）如果存在边信息，则可以容易地利用边信息，（iii）在有或没有边信息的情况下，推断二元体成员的潜在特征，（iv）它抵抗样本选择偏差，（v）它可以学习良好校准的概率，以及（vi）它可以扩展到非常大的数据集。据我们所知，没有一种现有的方法满足所有上述标准。特别是，许多方法假设标签是有序的，并且在存在边信息时忽略边信息。实验结果表明，该方法在协同过滤和链接预测等特殊情况下，与现有方法相比具有较强的竞争力，并且能够对标称数据进行准确的预测。
英文摘要：
In dyadic prediction, labels must be predicted for pairs (dyads) whose members possess unique identifiers and, sometimes, additional features called side-information. Special cases of this problem include collaborative filtering and link prediction. We present the first model for dyadic prediction that satisfies several important desiderata: (i) labels may be ordinal or nominal, (ii) side-information can be easily exploited if present, (iii) with or without side-information, latent features are inferred for dyad members, (iv) it is resistant to sample-selection bias, (v) it can learn well-calibrated probabilities, and (vi) it can scale to very large datasets. To our knowledge, no existing method satisfies all the above criteria. In particular, many methods assume that the labels are ordinal and ignore side-information when it is present. Experimental results show that the new method is competitive with state-of-the-art methods for the special cases of collaborative filtering and link prediction, and that it makes accurate predictions on nominal data.
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。
