Multi-objective Aligned Bidword Generation Model for E-commerce Search Advertising

TL;DR

This paper introduces MoBGM, a multi-objective model for generating aligned bidwords that improve relevance, authenticity, and revenue in e-commerce search advertising, outperforming existing methods.

Contribution

The paper presents a novel multi-objective aligned bidword generation model that optimizes relevance, authenticity, and revenue simultaneously for e-commerce search ads.

Findings

Significantly outperforms state-of-the-art methods in offline and online tests.

Creates substantial commercial value after deployment.

Proves robustness and feasibility in real-world scenarios.

Abstract

Retrieval systems primarily address the challenge of matching user queries with the most relevant advertisements, playing a crucial role in e-commerce search advertising. The diversity of user needs and expressions often produces massive long-tail queries that cannot be matched with merchant bidwords or product titles, which results in some advertisements not being recalled, ultimately harming user experience and search efficiency. Existing query rewriting research focuses on various methods such as query log mining, query-bidword vector matching, or generation-based rewriting. However, these methods often fail to simultaneously optimize the relevance and authenticity of the user's original query and rewrite and maximize the revenue potential of recalled ads. In this paper, we propose a Multi-objective aligned Bidword Generation Model (MoBGM), which is composed of a discriminator, generator, and preference alignment module, to address these challenges. To simultaneously improve the relevance and authenticity of the query and rewrite and maximize the platform revenue, we design a discriminator to optimize these key objectives. Using the feedback signal of the discriminator, we train a multi-objective aligned bidword generator that aims to maximize the combined effect of the three objectives. Extensive offline and online experiments show that our proposed algorithm significantly outperforms the state of the art. After deployment, the algorithm has created huge commercial value for the platform, further verifying its feasibility and robustness.