Time-homogeneous top-K ranking using tensor decompositions. (1st November 2020)

• Record Type: Journal Article
• Authors: Ataei, Masoud; Chen, Shengyuan; Yang, Zijiang; Peyghami, M. Reza
• Is Part Of: Optimization methods and software. Volume 35:Number 6(2020)
• Page Start: 1119
• Page End: 1143
• Publication Date: 2020-11-01
• Subjects: Rank aggregation -- top-K ranking -- Bradley–Terry–Luce (BTL) model -- rank centrality -- non-stationary spatio-temporal data analysis -- tensor decomposition -- cardinality-constrained portfolio optimization -- S&P500; Mathematical optimization -- Periodicals; Algorithms -- Periodicals; 519.7
• DOI: 10.1080/10556788.2019.1584623
• Languages: English
• ISSNs: 1055-6788

Abstract

ABSTRACT: Given N items, all having positive latent strengths, top- K ranking problem aims to identify the K items ( K ≤ N ) receiving the highest ranks based on partially revealed comparisons among the items. This problem has been widely studied in the case for which comparisons are performed independently. However, identifying the top- K rankings becomes a more intricate task when comparisons per se are performed along some temporal dimension. In this paper, we investigate potential impacts of temporality on sequences of top- K items and propose a time-homogeneous ranking scheme. Our framework relies mainly on tensor decompositions, rank centrality, and an innovative continuous extension of the Bradley–Terry–Luce (BTL) model. The proposed continuous BTL model extends the win/loss nature of the logistic model to a continuous setting, further reflecting preference degrees that may exist among the compared items. Our computations, which pertain to the analysis of S&P500 data from January 2008 to December 2017, confirm that the proposed top- K ranking scheme is an effective approach to optimize cardinality-constrained portfolios which involve large volumes of noisy and incomplete data.