Don’t Miss the Potential Customers! Retrieving Similar Ads to Improve User Targeting (2021)

Abstract: User targeting is an essential task in the modern advertising industry: given a package of ads for a particular category of products (e.g., green tea), identify the online users to whom the ad package should be targeted. A (ad package specific) user targeting model is typically trained using historical clickthrough data: positive instances correspond to users who have clicked on an ad in the package before, whereas negative instances correspond to users who have not clicked on any ads in the package that were displayed to them. Collecting a sufficient amount of positive training data for training an accurate user targeting model, however, is by no means trivial. This paper focuses on the development of a method for automatic augmentation of the set of positive training instances. Experimental results on two datasets, including a real-world company dataset, demonstrate the effectiveness of our proposed method.

## introduction user targeting is an essential task in the ecommerce advertising industry. informally, the goal of user targeting is to identify online users to whom a particular ad or ad package (i.e., a set of ads on a particular kind of products, such as green tea) should be targeted. figure 1 shows a pipeline through which the user targeting task is typically tackled. given an ad package that a company seeks to advertise, the company starts by randomly sampling a group of users from its customer database and displaying select ads in the package on the webpage(s) they visit. these users are then divided into two groups: clicking users and non-clicking users. clicking users are those who clicked on the ads and therefore expressed interest in them, while their non-clicking counterparts are those who did not click on the ads and are presumably not interested in the ads. these two groups of users then serve as positive and negative examples for training a user targeting model, which can then be used to predict whether a new user should be targeted for the given ad package. while this approach of using historical clickthrough data to automatically collect data for training a user targeting model is appealing at first glance, it has a key weakness: it may take time to collect enough data to train a reliable user targeting model, especially for long-tail ads (i.e., ads with few or no clicks). worse still, even after waiting long enough, we still cannot guarantee that there will be enough clicks to generate positive training examples. collecting sufficient positive training examples is critical to the success of this approach. to address this challenge, we put forward the following hypothesis: users who clicked on an ad for a particular product category (e.g., green tea) in the past are more likely to click on an ad for the same product category in the future. given this hypothesis, we can potentially expedite the collection of positive examples for training a user targeting model as follows. given a package of ads for a particular product category, we first identify ads for the same product category and then use their clicking users to augment the training data for training the user targeting model. the question, then, is: how can we automatically identify ads for the same product category as the one under consideration? one approach would be to train a classifier to classify an ad according to its product category. while this approach is straightforward, the resulting classifier will fail to classify an ad for a (new) product category that is not seen in the training data, in light of this weakness, we instead propose to learn how likely two ads are for the same product category. not only will this address the aforementioned question of identifying ads for the same product category as the one under consideration, but the resulting model will be applicable to new product categories. the next question is: how can we train a model to determine how likely two ads are for the same product category? since ads are displayed in the form of creatives that are typically composed of both texts and images, a reasonable solution to this problem should involve matching the texts and the images in the two ads. while algorithms for text matching @xcite @xcite , image matching @xcite , and text-image matching @xcite exist, none of them was developed specifically for ads. we therefore propose ion, a bimodal method that determines how likely two ads belong to the same product category, the key highlights of which include the design of (1) a semantics-enhanced image region extraction mechanism for identifying the region(s) of the image in an ad that is most relevant to the text, and (2) a dual-path fusion attention method for fusing the information extracted from the two modalities. in sum, our contributions in this paper are three fold. first, we hypothesize that users who clicked on an ad belonging to a particular product category in the past is more likely to click on an ad belonging to the same category in the future, and exploit this hypothesis to augment the positive instances used to train a user targeting model. second, we propose ion, a method for determining how likely two ads belong to the same product category, as a means to identify positive instances for user targeting. finally, we evaluate ion in terms of (1) its effectiveness in retrieving ads with the same product category and (2) its ability to improve a user targeting model via augmenting the training set using the positive instances it identifies. experiments on two datasets demonstrate its superiority to six baseline systems, providing suggestive evidence of its usefulness for the user targeting task. the rest of this paper is structured as follows. section 2 describes related work. in section 3, we present ion, our model for determining how likely two ads belong to the same product category. section 4 compares ion with state-of-the-art baselines on two datasets. finally, we conclude in section 5. ## related work works related to user targeting exist. unlike ours, they primarily focus on designing fancy models that are trained on a large amount of data @xcite @xcite . in contrast, we aim at solving the insufficient training data problem, which to our knowledge is an unexplored area of research. a crucial aspect of our work concerns the development of a method for determining how likely two ads belong to the same product category. below we will discuss related work on text matching, image matching, and text-image matching, even though none of the existing matching algorithms are specifically developed for ad matching. many text matching methods use an encoder such as rnns @xcite , cnns @xcite , recursive networks @xcite and transformer-based networks @xcite to embed input texts into vectors, possibly enhanced by attention @xcite , and then build a binary classifier to determine whether the inputs are similar. an exception is @xcite , whose matching method is based on rich alignment features. in general, however, the text in ads are often so ambiguous that it is difficult to determine which products are promoted. as for image matching, existing geometric feature detectors and descriptors can compute the similarity between images (e.g., lowe (2004), wang et al. (2018) ), and a matching mechanism based on cnns has been proposed to retrieval face images @xcite . however, a large portion of an ad image usually contain background objects, which make the extracted image features too noisy to accurately determine the underlying products being promoted. to perform text-image matching, some methods embed different modalities (e.g., texts and images) of the input into the same space and compute similarity from feature vectors @xcite @xcite , but they may be too coarse-grained to exploit local features, i.e., words and image regions. recent work @xcite @xcite split texts and images into fine-grained words and visual regions, and computes similarity by aligning the features of word semantics and those extracted from image regions, possibly with the help of attention @xcite spark your wrist with brand *, treasure your love forever and external knowledge @xcite . different from work on cross-modal matching, which measures the similarity between different modalities, our work focuses on fusing features from the texts and the image in an ad to create a multimodal representation. note that there is also related work that aims to generate multimodal vectors containing both text and image features for pre-training or classification @xcite @xcite , in which vectors from different modalities are concatenated to form the multimodal representation. rather than performing a simple concatenation, our work proposes an attention mechanism to fuse modalities in order to better identify the correspondence between words and image regions. in addition, while existing methods do not determine which words and image regions in an ad are relevant to the product under consideration and which ones are irrelevant/noisy, our method encodes words and extract image regions selectively so that those that are related to the product are given larger weights. ## method in this section, we describe our two-step method for determining how likely two ads belong to the same product category. during training, we train a model for learning an ad representation such that two ads that belong to the same product category have similar representations. after training, we can apply the resulting model as follows. given an ad in the test set, we retrieve the k ads that are most similar to it, where similarity is computed using a similarity metric applied to the representations of two ads. the rest of this section focuses on the first step, in which we train the model using multi-task learning to learn ad representations (the main task) simultaneously with keyword extraction from text (the auxiliary task). the model architecture is shown in figure 2 . given an ad composed of text and an image as input, the model first embeds the sequence of words using transformer (section 3.1). after that, a keyword-guided selective gate (ksg) mechanism is adopted to mine the semantics from these text representations (section 3.2), which are leveraged as clues for an attention module that reranks the generated image regions extracted by the yolov3 object detection module @xcite (section 3.3). finally, the model combines the re-ranked image regions and the distributed text representation through a dual-path fusion attention (dfa) layer to obtain a multimodal representation of the ad (section 3.4). below we introduce each of these modules in detail. ## evaluation the goals of our evaluation are two-fold. first, we evaluate ion's effectiveness in retrieving ads. second, we evaluate its ability to improve user targeting in real-world application scenarios.