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تسجيل مستخدم جديدA Time To Event Framework For Multi-touch Attribution
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Multi-touch attribution (MTA) estimates the relative contributions of the multiple ads a user may see prior to any observed
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In online advertising, users may be exposed to a range of different advertising campaigns, such as natural search or referral or organic search, before leading to a final transaction. Estimating the contribution of advertising campaigns on the users
journey is very meaningful and crucial. A marketer could observe each customers interaction with different marketing channels and modify their investment strategies accordingly. Existing methods including both traditional last-clicking methods and recent data-driven approaches for the multi-touch attribution (MTA) problem lack enough interpretation on why the methods work. In this paper, we propose a novel model called DeepMTA, which combines deep learning model and additive feature explanation model for interpretable online multi-touch attribution. DeepMTA mainly contains two parts, the phased-LSTMs based conversion prediction model to catch different time intervals, and the additive feature attribution model combined with shaley values. Additive feature attribution is explanatory that contains a linear function of binary variables. As the first interpretable deep learning model for MTA, DeepMTA considers three important features in the customer journey: event sequence order, event frequency and time-decay effect of the event. Evaluation on a real dataset shows the proposed conversion prediction model achieves 91% accuracy.
In online advertising, the Internet users may be exposed to a sequence of different ad campaigns, i.e., display ads, search, or referrals from multiple channels, before led up to any final sales conversion and transaction. For both campaigners and pu
blishers, it is fundamentally critical to estimate the contribution from ad campaign touch-points during the customer journey (conversion funnel) and assign the right credit to the right ad exposure accordingly. However, the existing research on the multi-touch attribution problem lacks a principled way of utilizing the users pre-conversion actions (i.e., clicks), and quite often fails to model the sequential patterns among the touch points from a users behavior data. To make it worse, the current industry practice is merely employing a set of arbitrary rules as the attribution model, e.g., the popular last-touch model assigns 100% credit to the final touch-point regardless of actual attributions. In this paper, we propose a Dual-attention Recurrent Neural Network (DARNN) for the multi-touch attribution problem. It learns the attribution values through an attention mechanism directly from the conversion estimation objective. To achieve this, we utilize sequence-to-sequence prediction for user clicks, and combine both post-view and post-click attribution patterns together for the final conversion estimation. To quantitatively benchmark attribution models, we also propose a novel yet practical attribution evaluation scheme through the proxy of budget allocation (under the estimated attributions) over ad channels. The experimental results on two real datasets demonstrate the significant performance gains of our attribution model against the state of the art.
Early detection of changes in the frequency of events is an important task, in, for example, disease surveillance, monitoring of high-quality processes, reliability monitoring and public health. In this article, we focus on detecting changes in multi
variate event data, by monitoring the time-between-events (TBE). Existing multivariate TBE charts are limited in the sense that, they only signal after an event occurred for each of the individual processes. This results in delays (i.e., long time to signal), especially if it is of interest to detect a change in one or a few of the processes. We propose a bivariate TBE (BTBE) chart which is able to signal in real time. We derive analytical expressions for the control limits and average time-to-signal performance, conduct a performance evaluation and compare our chart to an existing method. The findings showed that our method is a realistic approach to monitor bivariate time-between-event data, and has better detection ability than existing methods. A large benefit of our method is that it signals in real-time and that due to the analytical expressions no simulation is needed. The proposed method is implemented on a real-life dataset related to AIDS.
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