Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems
Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems
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DOI：
10.1109/bigdata.2018.8621994
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发表时间：
2018-12
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影响因子：
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通讯作者：
Junjie Liang;Jinlong Hu;Shoubin Dong;Vasant G Honavar
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文献类型：
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作者：
Junjie Liang;Jinlong Hu;Shoubin Dong;Vasant G Honavar
我们提出了Top-N-Rank，一种新颖的列表式学习排名模型，用于可靠地推荐N个排名靠前的项目。所提出的模型优化了广泛使用的累积折扣增益（DCG）目标函数的变体，其在两个重要方面不同于DCG：（i）它将DCG的评估仅限于排名列表中的前N个项目，从而消除了低排名项目对学习的排名函数的影响;以及（ii）它结合了允许模型利用具有不同可靠性或可信度级别的多种类型的隐式反馈的权重。由于得到的目标函数是非光滑的，因此优化具有挑战性，我们考虑使用传统的S形函数和校正线性单元（ReLU）对目标函数进行两个光滑近似。我们提出了一个家庭的学习排名算法（Top-N-Rank），工作与任何光滑的目标函数。然后，引入了一个更有效的变体，Top-N-Rank.ReLU，它有效地利用了ReLU函数的属性，将Top-N-Rank的计算复杂度从用户评分的平均项目数的二次降低到线性。我们使用两个广泛使用的基准测试，即MovieLens数据集和Amazon Video Games数据集的实验结果表明：（i）目标函数的“top-N截断”大大提高了前N个推荐的排名质量;（ii）与使用sigmoid相比，使用ReLU来平滑目标函数在排名质量和运行时间方面都有显着改善;以及（iii）Top-N-Rank.ReLU在排名质量方面大大优于表现良好的列表式排名方法。
We propose Top-N-Rank, a novel family of list-wise Learning-to-Rank models for reliably recommending the N top-ranked items. The proposed models optimize a variant of the widely used cumulative discounted gain (DCG) objective function which differs from DCG in two important aspects: (i) It limits the evaluation of DCG only on the top N items in the ranked lists, thereby eliminating the impact of low-ranked items on the learned ranking function; and (ii) it incorporates weights that allow the model to leverage multiple types of implicit feedback with differing levels of reliability or trustworthiness. Because the resulting objective function is non-smooth and hence challenging to optimize, we consider two smooth approximations of the objective function, using the traditional sigmoid function and the rectified linear unit (ReLU). We propose a family of learning-to-rank algorithms (Top-N-Rank) that work with any smooth objective function. Then, a more efficient variant, Top-N-Rank.ReLU, is introduced, which effectively exploits the properties of ReLU function to reduce the computational complexity of Top-N-Rank from quadratic to linear in the average number of items rated by users. The results of our experiments using two widely used benchmarks, namely, the MovieLens data set and the Amazon Video Games data set demonstrate that: (i) The "top-N truncation" of the objective function substantially improves the ranking quality of the top N recommendations; (ii) using the ReLU for smoothing the objective function yields significant improvement in both ranking quality as well as runtime as compared to using the sigmoid; and (iii) Top-N-Rank.ReLU substantially outperforms the well-performing list-wise ranking methods in terms of ranking quality.