We study the problem of recommending relevant products to users in relatively
resource-scarce markets by leveraging data from similar, richer in resource
auxiliary markets. We hypothesize that data from one market can be used to
improve performance in another. Only a few studies have been conducted in this
area, partly due to the lack of publicly available e
提出一种面向冷启动用户的内容为基础的跨领域推荐方法，利用极端多类分类的形式来预测用户对物品的评分标签，构建了一个融合了领域适应的体系结构和去噪自编码器的神经网络，实现了不依赖用户和物品重叠特征，不同领域之间的推荐， 在 Yahoo! JAPAN 的电影和新闻服务数据集上表现出超过交叉领域协同过滤方法的性能。