Recommender systems have been a valuable component in various online services such as e-commerce and entertainment. To provide an accurate top-N recommendation list of items for each target user, we have to answer a very basic question of how to model users’ feedback effectively. In this article, we focus on studying users’ explicit feedback, which is usually assumed to contain more preference information than the counterpart, i.e., implicit feedback. In particular, we follow two very recent transfer to rank algorithms by converting the original feedback to three different but related views of examinations, scores, and purchases, and then propose a novel solution called holistic transfer to rank (HoToR), which is able to address the uncertainty challenge and the inconvenience challenge in the existing works. More specifically, we take the rating scores as a weighting strategy to alleviate the uncertainty of the examinations, and we design a holistic one-stage solution to address the inconvenience of the two/three-stage training and prediction procedures in previous works. We then conduct extensive empirical studies in a direct comparison with the two closely related transfer learning algorithms and some very competitive factorization- and neighborhood-based methods on three public datasets and find that our HoToR performs significantly better than the other methods in terms of several ranking-oriented evaluation metrics.

中文翻译：

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整体转移到排名前 N 推荐

推荐系统一直是电子商务和娱乐等各种在线服务中的重要组成部分。要为每个目标用户提供准确的 top-N 推荐项目列表，我们必须回答一个非常基本的问题，即如何有效地对用户的反馈进行建模。在本文中，我们专注于研究用户的显式反馈，通常假设它包含比对方更多的偏好信息，即隐式反馈。特别是，我们通过将原始反馈转换为考试、分数和购买的三个不同但相关的视图来遵循最近的两个迁移到排名算法，然后提出了一种称为整体迁移到排名 (HoToR) 的新颖解决方案，它能够解决现有作品中的不确定性挑战和不便挑战。进一步来说，我们将评分分数作为一种加权策略来缓解考试的不确定性，我们设计了一个整体的单阶段解决方案来解决之前工作中两/三阶段训练和预测程序的不便。然后，我们在三个公共数据集上与两种密切相关的迁移学习算法和一些非常有竞争力的基于分解和邻域的方法进行直接比较，进行了广泛的实证研究，发现我们的 HoToR 在几个排名方面明显优于其他方法导向的评价指标。