论文标题
定向多元排名
Directional Multivariate Ranking
论文作者
论文摘要
用户提供的多倍率评估表明了用户对推荐项目的详细反馈，并能够对其偏好进行细粒度的了解。广泛的研究表明，对此类数据进行建模大大提高了建议的有效性和解释性。但是，由于排名对于建议至关重要，因此目前尚无原则解决方案来集体在不同方面产生多个项目排名。在这项工作中，我们提出了一个方向性的多光值排名标准，以使项目对多个方面进行整体排名。具体来说，我们将多光值评估视为用户的积分努力，构成了他/她对方面的偏好的向量。我们的关键见解是，两个多值偏好矢量之间差异向量的方向揭示了比较的成对顺序。因此，有必要使用多光值排名标准来保留此类成对比较的观测方向。我们进一步根据概率多元张量分解模型为多光值排名问题提供了完整的解决方案。对大型TripAdvisor多相关评级数据集和Yelp评论文本数据集进行大型TripAdvisor的全面实验分析证实了我们解决方案的有效性。
User-provided multi-aspect evaluations manifest users' detailed feedback on the recommended items and enable fine-grained understanding of their preferences. Extensive studies have shown that modeling such data greatly improves the effectiveness and explainability of the recommendations. However, as ranking is essential in recommendation, there is no principled solution yet for collectively generating multiple item rankings over different aspects. In this work, we propose a directional multi-aspect ranking criterion to enable a holistic ranking of items with respect to multiple aspects. Specifically, we view multi-aspect evaluation as an integral effort from a user that forms a vector of his/her preferences over aspects. Our key insight is that the direction of the difference vector between two multi-aspect preference vectors reveals the pairwise order of comparison. Hence, it is necessary for a multi-aspect ranking criterion to preserve the observed directions from such pairwise comparisons. We further derive a complete solution for the multi-aspect ranking problem based on a probabilistic multivariate tensor factorization model. Comprehensive experimental analysis on a large TripAdvisor multi-aspect rating dataset and a Yelp review text dataset confirms the effectiveness of our solution.
