CAMTA: Causal Attention Model for Multi-touch Attribution

• URL: http://arxiv.org/abs/2012.11403v2
• Date: Tue, 16 Feb 2021 13:35:46 GMT
• Title: CAMTA: Causal Attention Model for Multi-touch Attribution
• Authors: Sachin Kumar, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, Lovekesh Vig, Gautam Shroff
• Abstract summary: We propose CAMTA, a novel deep recurrent neural network architecture which is a casual attribution mechanism for user-personalised MTA. We demonstrate the superior performance of CAMTA with respect to prediction accuracy as compared to several baselines.
• Score: 25.258282793367453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Advertising channels have evolved from conventional print media, billboards and radio advertising to online digital advertising (ad), where the users are exposed to a sequence of ad campaigns via social networks, display ads, search etc. While advertisers revisit the design of ad campaigns to concurrently serve the requirements emerging out of new ad channels, it is also critical for advertisers to estimate the contribution from touch-points (view, clicks, converts) on different channels, based on the sequence of customer actions. This process of contribution measurement is often referred to as multi-touch attribution (MTA). In this work, we propose CAMTA, a novel deep recurrent neural network architecture which is a casual attribution mechanism for user-personalised MTA in the context of observational data. CAMTA minimizes the selection bias in channel assignment across time-steps and touchpoints. Furthermore, it utilizes the users' pre-conversion actions in a principled way in order to predict pre-channel attribution. To quantitatively benchmark the proposed MTA model, we employ the real world Criteo dataset and demonstrate the superior performance of CAMTA with respect to prediction accuracy as compared to several baselines. In addition, we provide results for budget allocation and user-behaviour modelling on the predicted channel attribution.

Related papers

• User Response in Ad Auctions: An MDP Formulation of Long-Term Revenue Optimization [13.868805489082701]
  We propose a new Markov Decision Process model for ad auctions to capture the user response to the quality of ads. By incorporating user response, our model takes into consideration all three parties involved in the auction (advertiser, auctioneer, and user)
  arXiv  Detail & Related papers  (2023-02-16T06:16:01Z)
• Boost CTR Prediction for New Advertisements via Modeling Visual Content [55.11267821243347]
  We exploit the visual content in ads to boost the performance of CTR prediction models. We learn the embedding for each visual ID based on the historical user-ad interactions accumulated in the past. After incorporating the visual ID embedding in the CTR prediction model of Baidu online advertising, the average CTR of ads improves by 1.46%, and the total charge increases by 1.10%.
  arXiv  Detail & Related papers  (2022-09-23T17:08:54Z)
• Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation [1.6859861406758752]
  We propose dynamic collaborative filtering Thompson Sampling (DCTS) to transfer knowledge among bandit models. DCTS exploits similarities between users and between ads to estimate a prior distribution of Thompson sampling. We show that DCTS improves click-through rate by 9.7% than the state-of-the-art models.
  arXiv  Detail & Related papers  (2022-08-25T08:13:24Z)
• Masked Transformer for Neighhourhood-aware Click-Through Rate Prediction [74.52904110197004]
  We propose Neighbor-Interaction based CTR prediction, which put this task into a Heterogeneous Information Network (HIN) setting. In order to enhance the representation of the local neighbourhood, we consider four types of topological interaction among the nodes. We conduct comprehensive experiments on two real world datasets and the experimental results show that our proposed method outperforms state-of-the-art CTR models significantly.
  arXiv  Detail & Related papers  (2022-01-25T12:44:23Z)
• Contextual Bandits for Advertising Campaigns: A Diffusion-Model Independent Approach (Extended Version) [73.59962178534361]
  We study an influence problem in which little is assumed to be known about the diffusion network or about the model that determines how information may propagate. In this setting, an explore-exploit approach could be used to learn the key underlying diffusion parameters, while running the campaign. We describe and compare two methods of contextual multi-armed bandits, with upper-confidence bounds on the remaining potential of influencers.
  arXiv  Detail & Related papers  (2022-01-13T22:06:10Z)
• CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution [16.854552780506822]
  We propose CausalMTA to eliminate the influence of user preferences. CaulMTA achieves better prediction performance than the state-of-the-art method. It also generates meaningful attribution credits across different advertising channels.
  arXiv  Detail & Related papers  (2021-12-21T01:59:16Z)
• Perceptual Score: What Data Modalities Does Your Model Perceive? [73.75255606437808]
  We introduce the perceptual score, a metric that assesses the degree to which a model relies on the different subsets of the input features. We find that recent, more accurate multi-modal models for visual question-answering tend to perceive the visual data less than their predecessors. Using the perceptual score also helps to analyze model biases by decomposing the score into data subset contributions.
  arXiv  Detail & Related papers  (2021-10-27T12:19:56Z)
• Modeling High-order Interactions across Multi-interests for Micro-video Reommendation [65.16624625748068]
  We propose a Self-over-Co Attention module to enhance user's interest representation. In particular, we first use co-attention to model correlation patterns across different levels and then use self-attention to model correlation patterns within a specific level.
  arXiv  Detail & Related papers  (2021-04-01T07:20:15Z)
• Multi-Channel Sequential Behavior Networks for User Modeling in Online Advertising [4.964012641964141]
  This paper presents Multi-Channel Sequential Behavior Network (MC-SBN), a deep learning approach for embedding users and ads in a semantic space. Our proposed user encoder architecture summarizes user activities from multiple input channels--such as previous search queries, visited pages, or clicked ads--into a user vector. The results demonstrate that MC-SBN can improve the ranking of relevant ads and boost the performance of both click prediction and conversion prediction.
  arXiv  Detail & Related papers  (2020-12-27T06:13:29Z)
• A novel auction system for selecting advertisements in Real-Time bidding [68.8204255655161]
  Real-Time Bidding is a new Internet advertising system that has become very popular in recent years. We propose an alternative betting system with a new approach that not only considers the economic aspect but also other relevant factors for the functioning of the advertising system.
  arXiv  Detail & Related papers  (2020-10-22T18:36:41Z)
• Interpretable Deep Learning Model for Online Multi-touch Attribution [14.62385029537631]
  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.
  arXiv  Detail & Related papers  (2020-03-26T23:21:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.