Utility-Oriented Reranking with Counterfactual Context
Authors: 
Yunjia Xi, Weiwen Liu, Xinyi Dai, Ruiming Tang, Qing Liu, Weinan Zhang, Yong YuAuthors Info & Claims
Article No.: 193, Pages 1 - 22
Abstract
As a critical task for large-scale commercial recommender systems, reranking rearranges items in the initial ranking lists from the previous ranking stage to better meet users’ demands. Foundational work in reranking has shown the potential of improving recommendation results by uncovering mutual influence among items. However, rather than considering the context of initial lists as most existing methods do, an ideal reranking algorithm should consider the counterfactual context—the position and the alignment of the items in the reranked lists. In this work, we propose a novel pairwise reranking framework, Utility-oriented Reranking with Counterfactual Context (URCC), which maximizes the overall utility after reranking efficiently. Specifically, we first design a utility-oriented evaluator, which applies Bi-LSTM and graph attention mechanism to estimate the listwise utility via the counterfactual context modeling. Then, under the guidance of the evaluator, we propose a pairwise reranker model to find the most suitable position for each item by swapping misplaced item pairs. Extensive experiments on two benchmark datasets and a proprietary real-world dataset demonstrate that URCC significantly outperforms the state-of-the-art models in terms of both relevance-based metrics and utility-based metrics.
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Publication History
Published: 31 July 2024
Online AM: 04 June 2024
Accepted: 24 May 2024
Revised: 07 April 2024
Received: 16 August 2022
Published in TKDD Volume 18, Issue 8
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