iSplit LBI: Individualized Partial Ranking with Ties via Split LBI

TL;DR

This paper introduces iSplit LBI, a novel individualized partial ranking method that accounts for personal preferences and ties, using a variable splitting algorithm to improve ranking accuracy and abnormal user detection.

Contribution

The paper proposes a unified framework and algorithm for personalized partial ranking with ties, incorporating abnormal user detection, which advances beyond global ranking models.

Findings

Outperforms state-of-the-art methods on simulated datasets

Effectively identifies abnormal users through abnormal signals

Demonstrates significant improvements on real-world datasets

Abstract

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called \ilbi. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://github.com/qianqianxu010/NeurIPS2019-iSplitLBI.