Incorporating side information into probabilistic matrix factorization using Gaussian Processes
Incorporating side information into probabilistic matrix factorization using Gaussian Processes
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发表时间：
2010
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通讯作者：
Ryan P. Adams;George E. Dahl;Iain Murray
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作者：
Ryan P. Adams;George E. Dahl;Iain Murray
概率矩阵分解 (PMF) 是一种强大的方法，用于对与成对关系相关的数据进行建模，可用于协同过滤、计算生物学和文档分析等领域。在许多领域，还有其他协变量可以帮助预测。例如，在对电影评级进行建模时，我们可能知道评级何时发生、用户住在哪里，或者电影中出现了哪些演员。然而，很难将这些辅助信息合并到 PMF 模型中。我们提出了一个框架，通过高斯过程先验将多个 PMF 问题耦合在一起，从而合并辅助信息。我们用在协变量空间上变化的函数替换标量潜在特征。 GP 对这些功能的优先要求要求它们能够平稳变化并共享信息。我们应用这种新方法来预测职业篮球比赛的得分，其中有关比赛地点和日期的辅助信息与结果相关。
Probabilistic matrix factorization (PMF) is a powerful method for modeling data associated with pairwise relationships, finding use in collaborative filtering, computational biology, and document analysis, among other areas. In many domains, there are additional covariates that can assist in prediction. For example, when modeling movie ratings, we might know when the rating occurred, where the user lives, or what actors appear in the movie. It is difficult, however, to incorporate this side information into the PMF model. We propose a framework for incorporating side information by coupling together multiple PMF problems via Gaussian process priors. We replace scalar latent features with functions that vary over the covariate space. The GP priors on these functions require them to vary smoothly and share information. We apply this new method to predict the scores of professional basketball games, where side information about the venue and date of the game are relevant for the outcome.