CREATER : CTR -driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning (2022)

Abstract: This paper focuses on automatically generating the text of an ad, and the goal is that the generated text can capture user interest for achieving higher click-through rate (CTR). We propose CREATER, a CTR-driven advertising text generation approach, to generate ad texts based on high-quality user reviews. To incorporate CTR objective, our model learns from online A/B test data with contrastive learning, which encourages the model to generate ad texts that obtain higher CTR. To make use of large-scale unpaired reviews, we design a customized self-supervised objective reducing the gap between pre-training and fine-tuning. Experiments on industrial datasets show that CREATER significantly outperforms current approaches. It has been deployed online in a leading advertising platform and brings uplift on core online metrics.

## introduction for businesses that want to promote their items and services, running online advertisements on advertising platforms is an effective way to achieve their marketing goals. with the aim of attracting users to know more about the displayed items, advertisers design ad creative (such as text, image and video). figure 1 is an illustration that shows the creative of an ad in news feed, which contains a text and an image. an appropriate creative design capturing user interest accurately can improve the ad's click-through rate (ctr). ctr is a key metric that quantifies the effect of an ad, because click is the precondition for any further actions such as sharing and purchase taken by users. thus designing ad creatives that can achieve higher ctr is crucial for ad delivery. traditionally, advertisers need to manually design the creative of each ad, and then resort to online a/b test results to continually refine initial creative for catching user interests. such trail-and-error process is labor-intensive and usually inefficient. in terms of the text in a creative, due to the variation characteristic of language expressions, it may need to be polished multiple times for obtaining an ideal one. to improve the efficiency of ad delivery for advertisers, especially for small advertisers that may not afford to hire professional writers, this paper focuses on automatically generating the text for an ad, and the goal is that the generated text can capture user interest for achieving higher ctr. there are several challenges to achieve this goal. (i) first, it is important to choose a suitable source for generating ad texts. a straightforward source is the corresponding item's title in landing page. however, a title is usually a mixture of item attributes while may not reflect user preference. in contrast, an ad text should contain insightful and informative contents that can arouse purchasing desire of users. (ii) second, most of current natural language generation (nlg) models are optimized using cross-entropy criterion, which is discrepant to the ctr metric we concern. to encourage the model to generate texts achieving higher ctr, there is a great need to incorporate ctr objective into training. (iii) last but not least, a well-trained nlg model usually need a large amount of paired data. however it is costly to collect sufficient humanwritten ad texts, especially for small advertisers, thus we are faced to low-resource problem. in this paper we propose creater, a ctr-driven advertising text generation approach, to address the above challenges. (i) first, we choose high-quality user reviews as input source for generation. compared to titles, user reviews intuitively contain contents that reflect real experience after purchasing. we also introduce an aspect term as input control code to improve the informativeness of generated text. (ii) second, to explicitly incorporate ctr objective during optimizing nlg models, we make use of collected user feedback through online a/b test. advertisers always perform online a/b test to compare two different texts of a same ad, where online ctr metric reflects the distinction between a relatively "good" text and a "bad" one. we employ contrastive learning for model optimization, which encourages our model to generate texts that can achieve high ctr. (iii) finally, to alleviate the lowresource problem, we make use of large-scale unpaired reviews to perform pre-training that provides warm-starting. we design a novel self-supervised objective customized to our scenario, which reduces the gap between pre-training and fine-tuning. creater has been deployed online in a leading advertising platform and it achieves significant improvement on core online metrics. the main contributions of this work are summarized as follows: ## problem formulation given a source x and a control code c for an ad, where the source is a high-quality user review of the ad item, the control code is an aspect term of such review to guide generation, we aim to learn a generation model p θ (y | x, c) that can produce an appropriate ad text y (where θ denotes trainable parameters of the model). our goal is that the generated ad text can capture user interest and attract users to know more about the ad item. ## proposed approach: creater figure 2 illustrates the workflow of our creater, and it consists of two stages. the first stage is controlled pre-training, which learns from unpaired user reviews to provide warm-starting for low-resource scenario. the second stage is contrastive fine-tuning, which further learns from online a/b test data that reflects user feedback, aiming to encourage the model to generate ad text that can achieve higher ctr.