A feature-pair-based associative classification approach to look-alike modeling for conversion-oriented user-targeting in tail campaigns

Mangalampalli, Ashish; Ratnaparkhi, Adwait; Hatch, Andrew; Bagherjeiran, Abraham; Parekh, Rajesh; Pudi, Vikram

doi:10.1145/1963192.1963236

Cited by 26 publications

(10 citation statements)

References 2 publications

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“…The rule-based methods [18,24] match similar users with specific demographic tags (age, gender, geography) or interests that are targeted by marketers. The core technical support in the background is user profile mining, which means that the interest tags are inferred from the user behaviour [21].…”

Section: Related Workmentioning

confidence: 99%

“…With designed common rules for all campaigns, previous rulebased approaches [18,24] match similar users with specific demographic tags (age, gender, geography) or interests that are targeted by marketers, which has unsatisfying performance. Using common pre-defined similarity function objectively such as Cosine or Jaccard for all campaigns, traditional similarity-based look-alike methods [3,11,16,17] usually find look-alike users by directly comparing all possible pairs between seed users and available users.…”

Section: Introductionmentioning

confidence: 99%

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Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising

Zhu

Liu

Xie

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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In recommender systems and advertising platforms, marketers always want to deliver products, contents, or advertisements to potential audiences over media channels such as display, video, or social. Given a set of audiences or customers (seed users), the audience expansion technique (look-alike modeling) is a promising solution to identify more potential audiences, who are similar to the seed users and likely to finish the business goal of the target campaign. However, look-alike modeling faces two challenges: (1) In practice, a company could run hundreds of marketing campaigns to promote various contents within completely different categories every day, e.g., sports, politics, society. Thus, it is difficult to utilize a common method to expand audiences for all campaigns. (2) The seed set of a certain campaign could only cover limited users. Therefore, a customized approach based on such a seed set is likely to be overfitting.In this paper, to address these challenges, we propose a novel twostage framework named Meta Hybrid Experts and Critics (Meta-Heac) which has been deployed in WeChat Look-alike System. In the offline stage, a general model which can capture the relationships among various tasks is trained from a meta-learning perspective on all existing campaign tasks. In the online stage, for a new campaign, a customized model is learned with the given seed set based on the general model. According to both offline and online experiments, the proposed MetaHeac shows superior effectiveness for both content marketing campaigns in recommender systems and advertising campaigns in advertising platforms. Besides, Meta-Heac has been successfully deployed in WeChat for the promotion of both contents and advertisements, leading to great improvement in the quality of marketing. The code has been available at https://github.com/easezyc/MetaHeac.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising

Zhu

Liu

Xie

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

View full text Add to dashboard Cite

show abstract

“…To solve this problem, we need an efficient user targeting strategy helping to extend the audience of those good long-tail contents. Look-alike modeling [10] is popular on audience extension technology which is widely used in online advertising [8]. As there is a candidate for audience extension and a certain amount of users who have clicked it, named seeds, the look-alike models essentially aim to find group of users who are similar to the seeds.…”

Section: Figure 1: Audience Extension In Recommender Systemmentioning

confidence: 99%

Real-time Attention Based Look-alike Model for Recommender System

Liu

Zhang

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Recently, deep learning models play more and more important roles in contents recommender systems. However, although the performance of recommendations is greatly improved, the "Matthew effect" becomes increasingly evident. While the head contents get more and more popular, many competitive long-tail contents are difficult to achieve timely exposure because of lacking behavior features. This issue has badly impacted the quality and diversity of recommendations. To solve this problem, look-alike algorithm is a good choice to extend audience for high quality long-tail contents. But the traditional look-alike models which widely used in online advertising are not suitable for recommender systems because of the strict requirement of both real-time and effectiveness. This paper introduces a real-time attention based look-alike model (RALM) for recommender systems, which tackles the challenge of conflict between real-time and effectiveness. RALM realizes real-time lookalike audience extension benefiting from seeds-to-user similarity prediction and improves the effectiveness through optimizing user representation learning and look-alike learning modeling. For user representation learning, we propose a novel neural network structure named attention merge layer to replace the concatenation layer, which significantly improves the expressive ability of multifields feature learning. On the other hand, considering the various members of seeds, we design global attention unit and local attention unit to learn robust and adaptive seeds representation with respect to a certain target user. At last, we introduce seeds clustering mechanism which not only reduces the time complexity of attention units prediction but also minimizes the loss of seeds information at the same time. According to our experiments, RALM shows superior effectiveness and performance than popular lookalike models. RALM has been successfully deployed in "Top Stories" Recommender System of WeChat, leading to great improvement on diversity and quality of recommendations. As far as we know,

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“…Based on user profiles, the advertisers can detect the users with similar interests to the known customers and then deliver the relevant ads to them. Such technology is referred as look-alike modelling [17], which efficiently provides higher targeting accuracy and thus brings more customers to the advertisers [29]. The current user profiling methods include building keyword and topic distributions [1] or clustering users onto a (hierarchical) taxonomy [29].…”

Section: Introductionmentioning

confidence: 99%

Implicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to CTR Estimation

Zhang¹,

Chen²,

Wang³

2016

Preprint

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User behaviour targeting is essential in online advertising. Compared with sponsored search keyword targeting and contextual advertising page content targeting, user behaviour targeting builds users' interest profiles via tracking their online behaviour and then delivers the relevant ads according to each user's interest, which leads to higher targeting accuracy and thus more improved advertising performance. The current user profiling methods include building keywords and topic tags or mapping users onto a hierarchical taxonomy. However, to our knowledge, there is no previous work that explicitly investigates the user online visits similarity and incorporates such similarity into their ad response prediction. In this work, we propose a general framework which learns the user profiles based on their online browsing behaviour, and transfers the learned knowledge onto prediction of their ad response. Technically, we propose a transfer learning model based on the probabilistic latent factor graphic models, where the users' ad response profiles are generated from their online browsing profiles. The large-scale experiments based on real-world data demonstrate significant improvement of our solution over some strong baselines.

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A feature-pair-based associative classification approach to look-alike modeling for conversion-oriented user-targeting in tail campaigns

Cited by 26 publications

References 2 publications

Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising

Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising

Real-time Attention Based Look-alike Model for Recommender System

Implicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to CTR Estimation

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