Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising

Conversion prediction plays an important role in online advertising since Cost-Per-Action (CPA) has become one of the primary campaign performance objectives in the industry. Unlike click prediction, conversions have different types in nature, and each type may be associated with different decisive factors. In this paper, we formulate conversion prediction as a multi-task learning problem, so that the prediction models for different types of conversions can be learned together. These models share feature representations, but have their specific parameters, providing the benefit of information-sharing across all tasks. We then propose Multi-Task Field-weighted Factorization Machine (MT-FwFM) to solve these tasks jointly. Our experiment results show that, compared with two state-of-the-art models, MT-FwFM improve the AUC by 0.74% and 0.84% on two conversion types, and the weighted AUC across all conversion types is also improved by 0.50%.Comment: SIGKD

Distributed Multi-Task Relationship Learning

Multi-task learning aims to learn multiple tasks jointly by exploiting their relatedness to improve the generalization performance for each task. Traditionally, to perform multi-task learning, one needs to centralize data from all the tasks to a single machine. However, in many real-world applications, data of different tasks may be geo-distributed over different local machines. Due to heavy communication caused by transmitting the data and the issue of data privacy and security, it is impossible to send data of different task to a master machine to perform multi-task learning. Therefore, in this paper, we propose a distributed multi-task learning framework that simultaneously learns predictive models for each task as well as task relationships between tasks alternatingly in the parameter server paradigm. In our framework, we first offer a general dual form for a family of regularized multi-task relationship learning methods. Subsequently, we propose a communication-efficient primal-dual distributed optimization algorithm to solve the dual problem by carefully designing local subproblems to make the dual problem decomposable. Moreover, we provide a theoretical convergence analysis for the proposed algorithm, which is specific for distributed multi-task relationship learning. We conduct extensive experiments on both synthetic and real-world datasets to evaluate our proposed framework in terms of effectiveness and convergence.Comment: To appear in KDD 201

Real-time bidding campaigns optimization using user profile settings

Real-time bidding is nowadays one of the most promising systems in the online advertising ecosystem. In this study, the performance of RTB campaigns is improved by optimising the parameters of the users\u27 profiles and the publishers\u27 websites. Most studies concerning optimising RTB campaigns are focused on the bidding strategy, i.e., estimating the best value for each bid. However, this research focuses on optimising RTB campaigns by finding out configurations that maximise both the number of impressions and the average profitability of the visits. An online campaign configuration generally consists of a set of parameters along with their values such as {Browser = Chrome , Country = Germany , Age = 20–40 and Gender = Woman }. The experiments show that when advertisers\u27 required visits are low, it is easy to find configurations with high average profitability. Still, as the required number of visits increases, the average profitability diminishes. Additionally, configuration optimisation has been combined with other interesting strategies to increase, even more, the campaigns\u27 profitability. In particular, the presented study considers the following complementary strategies to increase profitability: (1) selecting multiple configurations with a small number of visits rather than a unique configuration with a large number of visits, (2) discarding visits according to certain cost and profitability thresholds, (3) analysing a reduced space of the dataset and extrapolating the solution over the whole dataset, and (4) increasing the search space by including solutions below the required number of visits. RTB and other advertising platforms could offer advertisers the developed campaign optimisation methodology to make their campaigns more profitable