Query-Variant Advertisement Text Generation with Association Knowledge

TL;DR

This paper introduces a novel method for generating personalized advertisement texts tailored to diverse search queries by leveraging association knowledge to include low-frequency, personalized needs, improving attractiveness over traditional methods.

Contribution

It proposes a dynamic association mechanism that expands the model's receptive field with external knowledge, enabling effective generation of query-variant advertisement texts for personalized needs.

Findings

Generated texts are more attractive than baselines.

Model effectively incorporates low-frequency personalized needs.

Automatic and human evaluations confirm improvements.

Abstract

Online advertising is an important revenue source for many IT companies. In the search advertising scenario, advertisement text that meets the need of the search query would be more attractive to the user. However, the manual creation of query-variant advertisement texts for massive items is expensive. Traditional text generation methods tend to focus on the general searching needs with high frequency while ignoring the diverse personalized searching needs with low frequency. In this paper, we propose the query-variant advertisement text generation task that aims to generate candidate advertisement texts for different web search queries with various needs based on queries and item keywords. To solve the problem of ignoring low-frequency needs, we propose a dynamic association mechanism to expand the receptive field based on external knowledge, which can obtain associated words to be added to the input. These associated words can serve as bridges to transfer the ability of the model from the familiar high-frequency words to the unfamiliar low-frequency words. With association, the model can make use of various personalized needs in queries and generate query-variant advertisement texts. Both automatic and human evaluations show that our model can generate more attractive advertisement text than baselines.