Collaborative List-and-Pairwise Filtering from Implicit Feedback
Runlong Yu, Qi Liu, Yuyang Ye, Mingyue Cheng, Enhong Chen, Jianhui Ma
Abstract
The implicit feedback based collaborative filtering (CF) has attracted much attention in recent years, mainly because users implicitly express their preferences in many real-world scenarios. The current mainstream pairwise methods optimize the Area Under the Curve (AUC) and are empirically proved to be helpful to exploit binary relevance data, but lead to either not address the ranking problem, or not specifically focus on top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> recommendation. Although there exists the listwise method maximizes the Mean Reciprocal Rank (MRR), it has low efficiency and is not particularly adequate for general implicit feedback situations. To that end, in this paper, we propose a new framework, namely <i>Collaborative List-and-Pairwise Filtering (CLAPF)</i> , which aims to introduce pairwise thinking into listwise methods. Specifically, we smooth another well-known rank-biased measure called Mean Average Precision (MAP), and respectively combine two rank-biased metrics (MAP, MRR) with the pairwise objective function to capture the performance of top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> recommendation. Furthermore, the sampling scheme for CLAPF is discussed to accelerate the convergence speed. Our CLAPF framework is a new hybrid model that provides an idea of utilizing rank-biased measures in a pairwise way on implicit feedback. Empirical studies demonstrated CLAPF outperforms state-of-the-art approaches on real-world datasets.