面向协同过滤推荐的新型混合评分函数

协同过滤技术在现代推荐系统中得到了广泛的应用,其基本思想是相似的用户会喜欢相似的物品。评分函数(Score Function,SF)是协同过滤推荐模型的一个关键技术,用于评估用户对物品的喜好程度。然而,目前常用的评分函数存在如下缺陷,即内积评分函数难以有效捕捉用户与用户以及物品与物品的相似度,而欧几里德距离度量函数由于几何空间限制降低了模型的表达能力。文中提出了一种融合内积相似度和欧几里德距离度量的新颖的混合评分函数,并从理论上分析了此混合评分函数的性质,证明它能有效弥补现有评分函数的不足。此外,新的混合评分函数是一项通用技术,适用于诸多现有的推荐模型(如SVD++,MF,NGCF,CML等),能够提高模型的推荐质量。最后,在6个公开数据集上进行了大量实验,验证了新混合评分函数的优越性能。

Hybrid Score Function for Collaborative Filtering Recommendation

Collaborative Filtering has been widely used in modern recommendation systems,and it assumes that similar users prefer similar items.A key ingredient of CF-based recommendation model is the score function,which measures the preference of users on items.However,there are some shortages in the most popular score functions.The inner product score function fails to capture the user-user similarity and item-item similarity effectively,and Euclidean distance measurement function reduces the expressiveness of the model because of its geometrical restriction.This paper proposes a novel hybrid score function by mixing the inner product-based similarity and the Euclidean distance metric,and further theoretically analyze its properties,thus proving that the new score function can avoid the aforementioned shortages effectively.In addition,the new hybrid score function is a general technique and can help to improve the quality of recommendation for existing models(e.g.,SVD++,MF,NGCF,CML).Extensive empirical studies over 6 datasets demonstrate the superior performance of the proposed hybrid score function.