Interpretable Deep Learning Model for Online Multi-touch Attribution

• URL: http://arxiv.org/abs/2004.00384v1
• Date: Thu, 26 Mar 2020 23:21:40 GMT
• Title: Interpretable Deep Learning Model for Online Multi-touch Attribution
• Authors: Dongdong Yang, Kevin Dyer, Senzhang Wang
• Abstract summary: We propose a novel model called DeepMTA, which combines deep learning model and additive feature explanation model for interpretable online multi-touch attribution. As the first interpretable deep learning model for MTA, DeepMTA considers three important features in the customer journey. Evaluation on a real dataset shows the proposed conversion prediction model achieves 91% accuracy.
• Score: 14.62385029537631
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 user's journey is very meaningful and crucial. A marketer could observe each customer's 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.

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