In many cases it makes sense to model a relationship symmetrically, not
implying any particular directionality. Consider the classical example of a
recommendation system where the rating of an item by a user should
symmetrically be dependent on the attributes of both the user and the item. The
attributes of the (known) relationships are also relevant for predicting
attributes of entities and for predicting attributes of new relations. In
recommendation systems, the exploitation of relational attributes is often
referred to as collaborative filtering. Again, in many applications one might
prefer to model the collaborative effect in a symmetrical way. In this paper we
present a relational model, which is completely symmetrical. The key innovation
is that we introduce for each entity (or object) an infinite-dimensional latent
variable as part of a Dirichlet process (DP) model. We discuss inference in the
model, which is based on a DP Gibbs sampler, i.e., the Chinese restaurant
process. We extend the Chinese restaurant process to be applicable to
relational modeling. Our approach is evaluated in three applications. One is a
recommendation system based on the MovieLens data set. The second application
concerns the prediction of the function of yeast genes/proteins on the data set
of KDD Cup 2001 using a multi-relational model. The third application involves
a relational medical domain. The experimental results show that our model gives
significantly improved estimates of attributes describing relationships or
entities in complex relational models.

本文介绍一种完全对称的关系模型，它引入了每个实体的无限维潜在变量作为狄利克雷过程模型的一部分，并扩展了中餐馆过程以适用于关系建模。该模型在三个应用中进行评估，并在复杂的关系模型中提供了显着改进的估计关系或实体描述属性的结果。