Effective ad text can go a long way in attracting online users, and nudging them towards a purchase. However, writing effective ad text is an inherently creative process, and may require years of experience. This can be challenging for small businesses, who have limited advertising budgets and may not be able to afford expert ad copywriters. However, in an ad platform, there might be a mix of both well written and poorly written ads (as inferred from their online performance). Intuitively, it may be possible to help small businesses by comparing their ads with other semantically similar ads in the ad platform, and flagging ad text which is likely to perform relatively poor. In this paper, we build on this intuition, and introduce an ad text strength indicator (TSI). As shown in Figure 1, the core idea behind TSI is to fetch semantically similar ads in an ad platform, compare their predicted click-through-rates (CTRs) as a proxy for ad-effectiveness, and flag the input ad as weak if it has significantly stronger ads in its (semantic) neighborhood. Furthermore, such relatively stronger ads can be anonymized (e.g., by removing brand references), and shown to the advertiser as suggestions for improvement (i.e., suggestions to inspire better ads and not to be copied verbatim).
The TSI illustration in Figure 1 relies on two major components: (i) text-to-CTR prediction, and (ii) semantically similar ads retrieval. Both are challenging problems inherently linked to contextual understanding of ad text. For example, both ‘Get help from the BEST Home Security System!!!’ and ‘Need help with Social Security Benefits? Call Now!’ have the word security but the contexts are quite different. The similar ads neighborhood for the two ads, and the average CTRs in their neighborhood can also be different due to the difference in ad categories. Compared to bag-of-words and word2vec representations, BERT (Devlin et al., 2018) based text encoders are particularly helping in understanding such context. Leveraging this observation, for CTR prediction, we fine tune BERT for CTR prediction with ad impressions and clicks data. For similar ads retrieval, we fine tune a sentence-BERT model (Reimers and Gurevych, [n.d.]) trained for semantic-textual similarity. In particular, we introduce a weak labeling method for ad text pairs using the ad campaign setup information (e.g.
, two ads from the same advertiser, and the same category label are treated as similar). Such ad-specific fine tuning is effective in differentiating ads in different categories but with high word overlap. For example, ‘Online store for shoes!’ and ‘Online store for pets!’ are both online shopping ads and maybe be inferred as similar by a generic textual-similarity-model, but can be differentiated after ad-specific fine tuning based on our weak labeling method. In TSI, similar ads are not only used to decide whether an input ad is weak or strong, but are also used as suggestions for improvement. It is plausible to use text generation models to suggest the refined version of the input ad text (as in(Mishra et al., 2020; Hughes et al., [n.d.])). Although there has been significant progress in text generation, current generation methods for ads are not perfect all the time (Mishra et al., 2020). They can suffer from hallucinations (inserting irrelevant words), repetitions which affect fluency of the generated text, memorization (e.g., memorizing an ad from an advertiser and generating it verbatim), and high latency which may negatively impact the perception of an advertiser towards the ad platform. Due to such limitations, in this paper we adopt the similar ads approach for refinement suggestions.
Although the primary motivation behind TSI lies in helping small businesses with weaker ads, the core idea (as described above) essentially facilitates learning across all advertisers in the ad platform. In the online advertising industry, account managers are common interfaces between large advertisers (clients) and the ad platform. Account managers may launch multiple ad text variations (A/B tests for the same product being advertised), and study their performance over time to infer which text works best. For example, the dating ad insight that mentioning free sign up and the city (as illustrated in Figure 1) can boost the CTR may be inferred by a particular advertiser (and associated account managers). Advertisers may churn out over time due to numerous reasons (e.g., advertising budget cuts), and their learnings can be hard to track manually. In this context, TSI offers the ability to automatically learn across advertisers in a scalable manner (i.e., even for a churned-out advertiser, if the ad text has high predicted CTR, it can be used for relevant TSI suggestions).
Our main contributions can be summarized as follows.
We introduce a BERT based ad text-to-CTR prediction model and train it using clicks and impressions data from Verizon Media advertisers. The model provides significant AUC lift on top of bag-of-words based models ( AUC lift in the cold-start setting with new advertisers in the test set). The model also has the ability to ingest publisher information (i.e., the site where the ad will be shown) to provide publisher specific CTR predictions.
We introduce a sentence-BERT based semantic-ad-similarity model to compare two ad texts. The model training leverages ad campaign setup information (advertiser ID and category) to create weak labels for pairs of ads. The model achieves a precision@5 of (and precision@1 ) in terms of retrieving related ads from the same product category as the input ad. This is better than (unsupervised) TF-IDF, word2vec and SBERT based baselines. We use approximate nearest neighbours (ANN) to enable fast retrieval of similar ads from a pool of 50k ads in less than 0.5 seconds in a production environment.
In addition to TSI for English ads from the US, we study TSI for ads from Taiwan and Hong Kong, thereby developing multilingual extensions of the text-to-CTR and semantic-ad-similarity models.
We tested TSI online as a feature for both onboarding Verizon Media (Yahoo) DSP advertisers (with English ads only), and internal account teams. Our implementation had sub-second end-to-end latency in the DSP UI, providing its users feedback in real time. The account teams were strongly positive about the results (with an average rating of 4.3/5 across different strategists). Advertisers onboarding via the Verizon Media DSP UI also had positive interactions. In an online test from mid-January to mid-February 2021, of DSP advertisers (with English ads) seeing recommendations changed their ads based on ad text seen in TSI suggestions, and such adopters collectively observed a CTR lift compared to non-adopters who were exposed to recommendations. To the best-of-our-knowledge, the TSI feature for providing real time feedback on ad text along with suggestions is the first of its kind among major ad platforms, and it is still available for all Verizon Media advertisers.
The remainder of the paper is organized as follows. Section 2 covers related work, and Section 3 give an overview of the architecture. We cover our proposed methods for ad text-to-CTR in Sections 4. Similar ads retrieval and ad text anonymization are covered in 5. Experimental results are covered in Section 7, and we end with paper with a discussion in Section 8.
2. Related Work
2.1. Online advertising
In a typical online advertising setup (Bhamidipati et al., 2017; McMahan et al., [n.d.]; Zhou et al., 2019), advertisers design creatives (ad text and image) with the help of creative strategists to target relevant online users visiting websites (publishers) associated with the ad platform. The effectiveness of their creatives is measured via metrics such as click-through-rate (CTR ). CTR prediction models (McMahan et al., [n.d.]; Bhamidipati et al., 2017)
are trained to predict the probability of a click given an impression event (and features typically spanning user, publisher, context, device, ad creative, and advertiser). In this paper, we focus on click prediction models using only the textual information in an ad creative, and the publisher where the ad is being shown. The high level goal is to capture learnings across advertisers to surface best ad text practices, and give contextual creative guidance to advertisers. In this paper, we do not consider conversions data, and just focus on clicks data, since in most cases, clicks-data (in aggregated form) is accessible by the ad platform for making system-wide improvements.
2.2. Ad creative image and text understanding
Understanding ad images and text for the purposes of ad creative optimization is an area of active research. While A/B tests with a large pool of creatives to efficiently learn which creative works best (popularly known as dynamic creative optimization in the industry) (Li et al., 2010; Zhao et al., [n.d.]) are a common practice in the industry, recent works (Mishra et al., 2019; Zhou et al., 2020; Mishra et al., 2020; Hussain et al., 2017) have focused on models to understand ad creatives for gathering insights and automating the ad creation process. Understanding content in ad images was studied in (Hussain et al., 2017; Ye and Kovashka, 2018), where manual annotations were gathered from crowdsourced workers for: ad category, reasons to buy products advertised in the ad, and expected user responses given the ad. Leveraging the dataset in (Hussain et al., 2017), (Mishra et al., 2019; Zhou et al., 2020) studied recommending keyphrases for guiding a brand’s creative design using the Wikipedia pages of associated brands. In (Mishra et al., 2021), semantically similar ads were leveraged to automate ad image search given ad text. In the context of ad text generation, an encoder-decoder model for generating ad text based on an advertiser’s webpage was proposed in (Hughes et al., [n.d.]), and (Mishra et al., 2020) focused on refining input ad text using generation models. However, current ad text generation models are not perfect (Mishra et al., 2020), and suffer from hallucinations, lack of fluency, and high latency. Due to such reasons, we focus on a similar ads approach for refinement suggestions in this paper.
2.3. Contextual text embeddings
Traditionally, many language tasks such as translation are handled using recurrent neural networks, combined with attention mechanism. This reflects the fact that we tend to read a sentence from left to right. However, human also read words within context of other words, some of them could be quite far apart, instead of only left to right or right to left in a mechanical way. BERT is a recently proposed language model(Devlin et al., 2018), and has achieved state-of-the-art performance on a number of natural language understanding tasks. It makes use of the Transformer (Vaswani et al., 2017) encoder, a self-attention mechanism that learns contextual relations between words (or sub-words) in a text. As opposed to directional models (such as RNN, LSTM and GRU), which read the text sequentially (left-to-right or right-to-left), the Transformer encoder reads the entire sequence of words at once. Therefore it is considered bidirectional, but it is more accurate to say that it is non-directional. This characteristic allows the model to learn the context of a word based on all of its surroundings (left and right of the word). The success of BERT has sparked a subfield (BERTology) which includes RoBERTa (Liu et al., 2019), ALBERT (Lan et al., 2019), and ERNIE (Sun et al., 2019), etc. All these methods fall into the category of contextual text embedding networks. In this paper, we leverage pre-trained BERT models for both CTR prediction and retrieving similar ads given an input ad text.
The overall architecture for TSI is shown in Figure 2. As shown, calls to the CTR prediction model and similar ads retrieval model can be made in parallel for the input ad text, and their results can be combined to obtain the final TSI score (with suggestions).
The main components can be briefly described as follows.
CTR prediction model
We propose a BERT (Devlin et al., 2018) based CTR prediction model which takes in the ad text (e.g.
, concatenation of ad title, ad description, and call to action in the case of Verizon Media advertisers), and the publisher (for which CTR is to be estimated). We take the pre-trained uncased BERT base model released by Google(Devlin et al., 2018), and fine-tune it with ad impressions and clicks data. Details of the CTR prediction model are in Section 4.
Similar ads retrieval model
For similar ads retrieval, we assume a pool of existing ads ( for our experiments). Each ad in the pool has its original ad text, its anonymized version (without references to a brand), and the associated pCTR (predicted click-through rate, computed in advance from the CTR prediction model). The goal of similar ads retrieval is to find top
relevant (semantically similar) ads from the pool, given an input ad text. In this paper, we propose a semantic-ad-similarity model which produces ad text embeddings, and compares two ads using cosine similarity between their ad text embeddings. Our semantic-ad-similarity model is based on sentence-BERT(Reimers and Gurevych, [n.d.]) fine tuned with weak similarity labels from the ads data (additional details in Section 5).
The TSI block needs the results for CTR prediction and similar ads retrieval for a given input ad. Intuitively, the TSI block checks whether the input ad’s pCTR is low compared to pCTRs of other ads in its semantic neighborhood. If there are a significant number of superior ads (above a predefined pCTR difference threshold) in its neighborhood, the input ad is labeled weak (), else it is labeled strong (). In addition, for a weak input ad, anonymized versions of superior ads are shown as improvement suggestions. Additional details on the TSI logic are in Section 6.
4. Text-to-CTR prediction
The objective for our text-to-CTR prediction model was to come up with a click probability as defined below.
where is the model’s predicted click-through rate, is a binary label indicating click () or no click () for an ad impression event, is the concatenation of ad title, description and call to action, and indicates the publisher where the ad is being shown (e.g.
, Yahoo Mail). The loss function for our CTR prediction model is weighted binary cross entropy as defined below.
where is the weight for each sample (explained below). Each ad is treated as two samples, one with label meaning it is clicked, and another with label meaning it is not clicked. Therefore in the loss function (2) will be twice the number of our ads. Depending on the label, the weight assigned to each sample can be either the number of clicks if the label is , or the number of no clicks (impressions-clicks) if the label is .
The architecture for our proposed BERT fine-tuning (for CTR prediction) is shown in Figure 3
. The BERT model is essentially the encoder part of a Transformer network(Vaswani et al., 2017). BERT is a self-supervised language model which learns contextual relations between word pieces by training on multiple tasks including masked token prediction and next segment prediction. For fine-tuning BERT in our CTR prediction setup, we use the output from pooled_output111https://github.com/google-research/bert layer as the embedding for the text. The pooled_output layer takes the first token ([CLS] token) as the input followed by a dense layer with Tanh
activation. To improve the CTR prediction, we further leverage the publisher feature by concatenating the text embedding with the one-hot encoding of publishers (restricted tomost popular publishers), and then stack it with a dense layer of 64 nodes. In our experiments (details in Section 7.2), we observed that fine-tuning of the BERT model leads to a significant improvement in CTR prediction AUC compared to the case without any fine-tuning; for additional training details see the Appendix (reproducibility notes).
5. Similar ads retrieval
In this section, we first go over our similar ads retrieval model based on semantic-ad-similarity in Section 5.1, followed by Section 5.2 where we explain our method for anonymizing ads (which may be shown as TSI suggestions to advertisers after similar ads retrieval).
For similar ads retrieval, we assume a set (pool) of existing ads at least in the order of tens of thousands ( ads in our online experiments), and we need to retrieve top relevant ads from given an input ad. The relevance of an ad with respect to the input ad is defined as:
where is the (normalized) embedding of based on its ad text, and denotes the cosine similarity between the embeddings of and
. We use such cosine-similarity based relevance ranking framework (as opposed to having a neural network,e.g., DRMM (Yang et al., [n.d.]) score a pair of ad texts) due to the scale of our setup; it is convenient to use approximate nearest neighbor approaches (Bernhardsson, 2018) with cosine similarity as well. This choice is echoed by recent works on sentence-BERT based semantic-textual-similarity models (Reimers and Gurevych, [n.d.]). With (3) in place, the problem of similar ads retrieval boils down to learning embeddings which focus on the context of the ad (i.e., product being advertised). To obtain such embeddings in a supervised manner leveraging ads data, we propose: (i) a weak labeling method for pairs of ad text (details on Section 5.1.1), and (ii) fine-tuning a sentence-BERT model with such weakly labeled pairs (details in Section 5.1.2).
5.1.1. Weak labeling using ads data
There is no prior data set with pairs of ads labeled as similar. However, it is plausible to exploit the structure of campaign setup in ad platforms to come up with weak similarity labels for ad text pairs. Figure 4 shows an example of how an ad campaign may be setup: the advertiser can have multiple campaigns (typically tied to product category but not necessarily). Each campaign can have ad groups associated with it where the user targeting criteria (e.g., location targeting) is specified, and finally within an ad group there may be multiple ad IDs (each with different text or image but typically tied to the same product). The category (e.g., IAB category (iab, [n.d.])
) associated with each ad ID may be self-declared by the advertiser (noisy) or may be inferred via a category classifier222In our experiments, we consider categories corresponding to the top-level IAB categories (iab, [n.d.]). This is not fine grained enough to identify ads advertising the same product type, e.g., both shoes and shirts ads may be in the same (apparel) category.. Following the intuition in Figure 4, we weakly label a pair of ads from ads pool as positive (similar) if both of them are from the same advertiser, and have the same category. If they have different categories, and are from different advertisers then the pair is marked negative (dissimilar). We arrived at this combination (i.e., choosing the advertiser to be same as opposed to the same campaign or ad group) via offline experiments (discussed in Section 7). Given a large advertiser base, our weak labeling data can easily generate sufficient data to fine-tune a sentence-BERT model as discussed below.
5.1.2. Sentence-BERT fine-tuning with weak labels
Given weakly labeled pairs of ad text, we build on the work in (Reimers and Gurevych, [n.d.]) to obtain fine tuned sentence-BERT representation for an ad text. Sentence-BERT adds a pooling operation to the output of BERT to derive a sentence embedding as shown in Figure 5. We take a pretrained sentence-BERT model for semantic-textual-similarity (with mean-pooling), and fine-tune it with the weakly labeled ad text pairs. For fine-tuning, we use use mean-square (regression) loss for a pair as defined below:
where weak .
As we describe later in Section 7, this loss function yielded the best (offline) results compared to cross-entropy loss, and triplet loss (by using triplets of positive, positive, and negative text as in (Reimers and Gurevych, [n.d.])). We will refer to our proposed model (as desribed above) as SAS-SBERT (Semantic-Ad-Similarity SBERT).
5.2. Anonymizing ad text
In our setup, retrieved similar ads may be shown as suggestions for improvement for an input ad text. To discourage copying ads verbatim, and to prevent users of TSI from identifying a particular brand (which may lead to biases in TSI adoption), we anonymize the ads pool
by removing references to a brand in ad text. For anonymizing ad text, we use a block list based approach (with brand and product names) coupled with off-the-shelf named entity recognition (NER) tools(Honnibal et al., 2020).
TSI’s core motivation lies in detecting poorly written ads, and providing suggestions for improvement. However, instead of rules (thresholds) defined on absolute pCTR values, we focus on the (semantic) neighborhood of the input ad text (where its neighbors are the top- retrieved similar ads from the pool ). The goal of TSI is to provide an estimate of the goodness (pCTR) of the input ad relative to its neighbours (similar ads). For our experiments, we used the following rule based algorithm for TSI.
Retrieve top neighbors for the input ad, and obtain the pCTRs of the input and the neighbors,
compute median pCTR of ads above input ad in pCTR, and
if the above median is more than (we use ) of the input ad pCTR, TSI = 0 (means input is weak), else TSI = 1 (input is strong). The corresponding neighbors (above in pCTR compared to input) are shown as ad text suggestions (anonymized versions) for improving the input ad.
Figure 6 illustrates the neighborhood-based TSI as described above.
As we describe later in Section 7.4, the choice of is crucial in determining the chances of an ad text getting TSI=0 score (higher can lead to fewer neighbors with pCTR better than the threshold, and a lower chance of getting TSI=0 score).
For offline experiments with our CTR prediction model and semantic-ad-similarity model, we collected data for one month from Verizon Media ad platform. For each ad-ID, the following fields were collected: ad title, ad description, call to action (CTA) text, clicks, impressions, and publisher. The ad text was defined as the concatenation of ad title, description and call-to-action with a period mark. The publishers were limited to top publishers by impression count. Furthermore, we sampled the data collected (from US advertisers) to prepare a set of ad IDs spanning over advertisers. For the CTR prediction model, two datasets were created: (i) warm-start, (ii) cold-start. Warm start dataset has train-test-validation random splits with the possibility of ads from the same advertiser occurring in multiple splits. In the cold start dataset, an advertiser can occur only in one split (i.e., the test advertisers do not occur in train data). For experiments with ads from Taiwan and Hong Kong (Chinese ad text), we repeated the above procedure to get a sample of ads for Taiwan, and ads for Hong Kong.
7.2. CTR prediction
We evaluate our fine-tuned BERT model (as in Section 4) with two experimental settings: warm-start (random split) setting and cold-start setting. For random split setting, we randomly do a 80/6/14 train/validation/test split. For cold-start setting, we group ads based on their advertisers, and split into train/validation/test. Before going over the results, we explain below our metrics and baselines.
Our CTR prediction model can be used not just for predicting the CTR of an ad, but also as an ad text ranker for advertisers intending to rank multiple ad text variations by pCTR. Therefore, we use AUC to measure how our estimated CTRs perform in terms of classifying clicks and not-clicks, and use Kendall Tau-b coefficient (KTC) (Kendall, 1945) and Spearman’s rank correlation coefficient (SRCC) (Zwillinger and Kokoska, 2000) to measure how well the estimated CTR’s order aligns with ad text ranking derived from the ground truth CTRs.
KTC is a measure of the correspondence between two sets of rankings and . It is defined as
where is the number of concordant pairs, the number of discordant pairs, the number of pairs with a tie only in ranking array , and the number of ties only in , and is the number of entries in and . If a tie occurs for the same pair in both and . Values of KTC close to 1 indicate strong agreement, values close to -1 indicate strong disagreement. SRCC is a nonparametric measure of the monotonicity of the relationship between two datasets and . Like KTC, its value also varies between -1 and 1 with 0 implying no correlation. Correlations of -1 or 1 imply an exact monotonic relationship. Positive correlations imply that as increases, so does . Negative correlations imply that as increases, decreases.
Note that in calculating AUC, we treat each impression as a sample separately. Two impressions of the same ad could be clicked and not-clicked respectively, but our model could only give one probability for both. Hence the AUC has an upper-bound, which is obtained by setting the pCTR to be the ground truth CTR, that is significantly less than 1. Considering this, we introduce an additional metric named relative AUC to evaluate how close our estimated CTR is to the actual CTR. The relative AUC is defined as
In measuring KTC and SRCC, on the other hand, we treat each ad as one sample, and measure how well our predicted CTRs are, in terms of giving the same ordering as the actual CTRs.
Table 1 shows offline results for the warm start setting (random split).
As can be observed, NBLR performs better than logistic regression (LR) due to the introduction of the naive Bayes ratio. Fine-tuned BERT333For BERT fine-tuning, we initialize our model using the uncased BERT base checkpoint. The batch size is set to 32, the learning rate is set to , and the seq_length is set to 100. We run 20 epochs to fine-tune the parameters.
, and the seq_length is set to 100. We run 20 epochs to fine-tune the parameters.and NBLR both perform well. While BERT is better in terms of AUC, NBLR achieves better KTC and SRCC. By comparing row 3 and row 4, we find that BERT model without fine-tuning performs much worse than fine-tuned BERT. It is therefore necessary to fine-tune BERT for the CTR prediction task. The results in row 3 and row 5 indicate that CTR is biased by the publisher444In addition to the publisher, there can be other confounding factors affecting the CTR, like the choice of ad image, location of user, and user’s device. We focus on publisher in this paper as it is a possible input in our end application, but it is straightforward to integrate the other features listed above in our framework. in the case of BERT fine-tuning. We observed the same publisher bias when a linear NBLR model was trained using set 2 features, and the performance was similar to that of the fine-tuned BERT model. Based on this observation, we include publisher as an additional input (can be optional) in our online tests with the TSI feature. We also explore the ensemble of NBLR with BERT. However, the results in row 6 of Table 1 indicate that an ensemble model does not provide any performance gains. In addition, we want to stress that although the best AUC we get here is only 0.735, it is worth mentioning that the upper bound AUC for the test data (by using the ground truth CTR) is 0.7812.
|6||BERT+NBLR||Set 2, 1||0.7352||94.11%||0.4414||0.6112|
The results for the cold-start setting are listed in Table 2. The upper-bound AUC obtained by the ground truth CTR is 0.7252. As can be observed, using BERT leads to increase in AUC, and increase in KTC and SRCC respectively. We believe this improvement in performance is due to the fact that the BERT model is pre-trained using a large amount of text data, and its language modeling tasks allow BERT to learn lexical similarity in word piece representation. Due to this, BERT is less sensitive to cold-start problem compared with NBLR. Because of the superior performance of the fine-tuned BERT model in the cold start setting (which is the more likely case for an inexperienced onboarding advertiser), we use the same for our online tests with the TSI feature.
Multilingual extension for CTR prediction model
We extended our CTR predictor to ads from Taiwan and Hong Kong, both of which use traditional Chinese text. The performance of our CTR prediction model is shown in Table 3
. For Chinese ads, we fine-tuned a BERT model pre-trained for Chinese text on Wikipedia (provided by TensorFlow Hub555https://tfhub.dev/tensorflow/bert_zh_L-12_H-768_A-12/1). Our model was fine-tuned using both Hong Kong and Taiwan ads data.
|Testing Data||AUC||Relative AUC||KTC||SRCC|
We evaluated performance on three different testing datasets: (i) Hong Kong and Taiwan mixed (HK+TW), (ii) Hong Kong only (HK), and (iii) Taiwan only (TW) data. We obtained CTR prediction (using fine-tuned BERT) AUCs comparable to the English ads model (0.74) for the Chinese dataset.
7.3. Similar ads retrieval
For the purposes of evaluating the similar ads retrieval model (offline), we consider different notions of similarity. With reference to the ad campaign setup hierarchy in Figure 4, two ads in the same ad group form our strongest notion of similarity, and two ads in the same category form our weakest notion of similarity (same campaign, and same advertiser cases are of intermediate nature). For a given similarity notion (type), we measure the precision@k of the list of retrieved ads given an input ad.
As baselines, we consider sentence (ad text) representations using TF-IDF, word2vec (Mikolov et al., 2013) based sentence representation (i.e.
, mean of word2vec vectors of tokens) as offered in Spacy(Honnibal et al., 2020), and the sentence-BERT model (Reimers and Gurevych, [n.d.]). Table 4 shows the category precision@ (i.e., the fraction of top similar ads which are in the same category as the input ad) for the baselines versus the proposed model (semantic-ad-similarity using SBERT fine tuned with ads data).
The results for the proposed model are based on the cosine similarity loss defined in (4) considering two ads in the same category and from the same advertiser as a weakly positive label while training666For training SAS-SBERT for English ads, we used the distilbert-base-nli-stsb-mean-tokens model as our initial model (Reimers and
Gurevych, [n.d.]) . In comparison, BERT (base and large), and RoBERTa (base and large) had marginal benefits with longer training time.
The SBERT hyperparameters were: positive-to-negative sampling ratio = 1:30 , batch size = 30, and epochs = 10.
. In comparison, BERT (base and large), and RoBERTa (base and large) had marginal benefits with longer training time. The SBERT hyperparameters were: positive-to-negative sampling ratio = 1:30 , batch size = 30, and epochs = 10.. As shown in Table 4, the proposed model clearly outperforms the baselines (even sentence-BERT); the differences are particularly significant as increases (the proposed model has lift over baselines at ). To understand how the proposed model fares for different notions of similarity (following the ad campaign setup in Figure 4), Table 5 reports the precision at results (for ) for multiple notions of similarity (i.e., ad group, campaign, advertiser, and category).
Table 5 also shows the impact of different weak labeling strategies on the precison@k for different notions of similarity: for advertiser-cat, two ads with the same advertiser ID and category are labeled positive, while for campaign-cat, and adgroup-cat, the restriction is changed to campaign ID and adgroup ID respectively (instead of advertiser ID). The advertiser-cat combination works best for similarity at an advertiser and category level, while campaign-cat works best for the campaign and ad-group level notion of similarity. Table 5 also shows that the proposed model retrieves ads across advertisers during similar ads retrieval. For example, if the category is 0.93, and the advertiser is , roughly fraction of test cases have their closest (most similar ad) fetched from a different advertiser (sharing the same category). In addition to the above, we explored cross-entropy loss and triplet loss (Reimers and Gurevych, [n.d.]) with our best weak labeling strategy (i.e., using advertiser ID and category), and found that cosine similarity loss (as defined in (4)) is significantly better (empirical evidence from our datasets). Our evaluation was limited to internal datasets due to the lack of public datasets with ad text and metadata (e.g., category, advertiser, and campaign information); exisiting e-commerce datasets with product titles (short, incomplete English sentences) were not suitable for our setup which focuses on native and display ads (with longer text length, complete English sentences).
Using the multilingual (xlm) version of sentence-BERT777For training SAS-BERT on ads from Taiwan and Hong Kong, we used distiluse-base-multilingual-cased-v2 in https://www.sbert.net/docs/pretrained_models.html. For both Taiwan and Hong Kong ads, we used: positive-to-negative sampling ratio = 1:30, batch size = 30, and epochs = 3., we trained SAS-SBERT models for ads from Taiwan and Hong Kong. For Taiwan ads, sentence-BERT had a of , while SAS-SBERT achieved a of ( lift). For Hong Kong ads, sentence-BERT had a of , while SAS-SBERT achieved a of ( lift). Clearly, the SAS-SBERT lifts for Chinese ad text are even stronger compared to their lifts in the case of US based ads in English.
7.4. TSI recommendation rate (offline)
For TSI, the relative pCTR difference threshold (as defined in Section 6) is an important tuning parameter deciding the fraction of ads eligible for recommendations. To get an offline estimate of the fraction of ads eligible for recommendations (i.e., TSI), we computed TSI for each ad in the test set ( train-test split), considering the train set as the existing pool of ads. In other words, we fetched top similar ads from the pool of train ads, and computed TSI by comparing their pCTRs with the test ad’s pCTR. For similar ads retrieval, the minimum cosine similarity threshold was set to . Figure 7 shows the variation of recommendation rate (fraction of ads shown recommendations) with respect to the relative pCTR difference threshold. As increases, there are fewer ads in a given input’s neighborhood with relative pCTR above the threshold; hence the chances of being labeled weak (TSI=0) decreases. At , of test ads are eligible for recommendations; in other words, of test ads are such that they have at least one neighbor who has better pCTR.
7.5. TSI online results
Our online results for TSI can be categorized into two parts: (i) aggregated feedback from internal account teams and creative strategists at Verizon Media, and (ii) session-wise log analysis of advertiser (external) interactions with the TSI feature in the Verizon Media (DSP) ad platform. For online testing, the proposed BERT based CTR predictor was used, whereas for similar ads retrieval the word2vec version (as described in Section 7.3) was used due to latency constraints. End-to-end online latency for the TSI feature was below 1 second, giving the user real time feedback with their ad text.
7.5.1. Account team feedback
We deployed an internal version of TSI for Verizon Media account teams and creative strategists, where they had access to the pCTR scores, all similar ads (and not just suggestions with better pCTR), and final TSI scores with recommendations. The aggregate rating collected from account strategists on the feature as a whole was . This was based on unique ad texts tried by the account teams (collectively) over a span of 2 months. Qualitative feedback revolved around TSI being fast, and proving to be a helpful tool. Critical feedback included the usage of conversion prediction for differentiating between click-bait text and genuinely well written ad text, and the detection of subtle yet abusive content which may have high CTRs logged in data – both are directions for future research for the authors.
7.5.2. Advertiser session log analysis
Verizon Media (Yahoo) DSP ad platform is a major ad platform in the online advertising industry. For online tests with real advertisers, we deployed the TSI feature to assist onboarding Verizon Media DSP advertisers. Figure 8 shows a screen shot of the DSP TSI feature for a dating ad example.
We collected sampled data spanning over advertiser sessions with English ads (from mid-January to mid-February 2021); a session being a continuous series of online events in the DSP UI (involving the same advertiser) with time gaps no more than 30 minutes between consecutive events. The following statistics were obtained from the above sessions.
In of sessions, at least one of the ad text variations got a TSI score of , and the advertiser was shown recommendations. This is reasonably close to the offline recommendation rate estimate of with the pCTR difference threshold as explained in Section 7.4.
In of the sessions with TSI recommendations, after seeing the TSI recommendations, the advertiser incorporated a new word (excluding stop-words in NLTK (Bird et al., 2009)) in their ad text. This new word was not present before in the original ad text, but was borrowed from the TSI recommendations by the advertiser. This is a proxy to automatically gauge the influence of the recommendation on the ad text. Furthermore, the fraction of advertisers who adopted recommendations, collectively saw a CTR lift over advertisers who were exposed to recommendations but did not adopt.
Since the advertisers eventually go ahead with one single ad text, it is hard to compare the CTRs of the original ad and the final ad after interacting with TSI (the CTR lift reported above is computed by treating adopters and non-adopters separately, but the comparison is not ideal). The ideal way to set up the comparison would be to integrate TSI with a dynamic creative optimization (DCO) framework; this is beyond the scope of this paper (but is a part of our on-going and future work). To get a qualitative perspective, we manually analyzed a few sessions to find examples where TSI convinced the advertiser towards making positive changes. We have described below two such examples (with anonymization as applicable) in Figure 9. In the top example in Figure 9, a gaming advertiser noticed a TSI suggestion (another successful gaming ad) which highlighted ‘Must-Play’ and had ‘Play Now’ as the call-to-action. Subsequently, the advertiser changed the original ad to incorporate the above changes (as shown in Figure 9), and proceeded with a strong TSI score. In the bottom example, the advertiser introduced words on trust and reputation in the ad for lawyers. As mentioned above, of advertiser sessions which saw TSI suggestions, incorporated changes; this demonstrates the significant impact of TSI on advertisers, and Figure 9 just shows two such examples. It should also be noted that the TSI suggestions are essentially anonymized versions of older ads with significant number of impressions and clicks in the past, i.e., ads which are already public in nature (as opposed to learning from ads which have not been exposed to the public yet).
Our online results demonstrate the efficacy of TSI in detecting ad text likely to have (relatively) low CTR, and inspiring advertisers to make positive changes in their ad text. We are continuously collecting data from TSI (still an actively used feature in the Verizon Media DSP UI at the time of this submission), and our next steps include using such data for training text generation models along the lines of (Mishra et al., 2020) to minimize the effort needed from the advertiser’s side. Increasing our ads data pool to multiple months while accounting for CTR seasonality, is another direction for future research.
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