DCRMTA: Unbiased Causal Representation for Multi-touch Attribution

• URL: http://arxiv.org/abs/2401.08875v2
• Date: Mon, 5 Feb 2024 08:43:29 GMT
• Title: DCRMTA: Unbiased Causal Representation for Multi-touch Attribution
• Authors: Jiaming Tang
• Abstract summary: Multi-touch attribution (MTA) currently plays a pivotal role in achieving a fair estimation of the contributions of each advertising to-wards conversion behavior. Previous works attempted to eliminate the bias caused by user preferences to achieve the unbiased assumption of the conversion model. This paper re-defines the causal effect of user features on con-versions and proposes a novel end-to-end ap-proach, Deep Causal Representation for MTA.
• Score: 0.2417342411475111
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-touch attribution (MTA) currently plays a pivotal role in achieving a fair estimation of the contributions of each advertising touchpoint to-wards conversion behavior, deeply influencing budget allocation and advertising recommenda-tion. Previous works attempted to eliminate the bias caused by user preferences to achieve the unbiased assumption of the conversion model. The multi-model collaboration method is not ef-ficient, and the complete elimination of user in-fluence also eliminates the causal effect of user features on conversion, resulting in limited per-formance of the conversion model. This paper re-defines the causal effect of user features on con-versions and proposes a novel end-to-end ap-proach, Deep Causal Representation for MTA (DCRMTA). Our model focuses on extracting causa features between conversions and users while eliminating confounding variables. Fur-thermore, extensive experiments demonstrate DCRMTA's superior performance in converting prediction across varying data distributions, while also effectively attributing value across dif-ferent advertising channels.

Related papers

• Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model [66.91323540178739]
  Sequential recommendation (SR) aims to predict items that users may be interested in based on their historical behavior. We revisit SR from a novel information-theoretic perspective and find that sequential modeling methods fail to adequately capture randomness and unpredictability of user behavior. Inspired by fuzzy information processing theory, this paper introduces the fuzzy sets of interaction sequences to overcome the limitations and better capture the evolution of users' real interests.
  arXiv  Detail & Related papers  (2024-10-31T14:52:01Z)
• Generative Diffusion Models for Sequential Recommendations [7.948486055890262]
  Generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have shown promise in sequential recommendation tasks. This research introduces enhancements to the DiffuRec architecture to improve robustness and incorporates a cross-attention mechanism in the Approximator to better capture relevant user-item interactions.
  arXiv  Detail & Related papers  (2024-10-25T09:39:05Z)
• Influence Functions for Scalable Data Attribution in Diffusion Models [52.92223039302037]
  Diffusion models have led to significant advancements in generative modelling. Yet their widespread adoption poses challenges regarding data attribution and interpretability. In this paper, we aim to help address such challenges by developing an textitinfluence functions framework.
  arXiv  Detail & Related papers  (2024-10-17T17:59:02Z)
• MITA: Bridging the Gap between Model and Data for Test-time Adaptation [68.62509948690698]
  Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. We propose Meet-In-The-Middle based MITA, which introduces energy-based optimization to encourage mutual adaptation of the model and data from opposing directions.
  arXiv  Detail & Related papers  (2024-10-12T07:02:33Z)
• Mitigating Shortcut Learning with Diffusion Counterfactuals and Diverse Ensembles [95.49699178874683]
  We propose DiffDiv, an ensemble diversification framework exploiting Diffusion Probabilistic Models (DPMs) We show that DPMs can generate images with novel feature combinations, even when trained on samples displaying correlated input features. We show that DPM-guided diversification is sufficient to remove dependence on shortcut cues, without a need for additional supervised signals.
  arXiv  Detail & Related papers  (2023-11-23T15:47:33Z)
• Ad-Rec: Advanced Feature Interactions to Address Covariate-Shifts in Recommendation Networks [2.016365643222463]
  Cross-feature learning is crucial to handle data distribution drift and adapt to changing user behaviour. This work introduces Ad-Rec, a network that leverages feature interaction techniques to address covariate shifts. Our approach improves model quality, accelerates convergence, and reduces training time, as measured by the Area Under Curve (AUC) metric.
  arXiv  Detail & Related papers  (2023-08-28T21:08:06Z)
• Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation [50.93536377097659]
  This paper introduces the Causal Disentangled Variational Auto-Encoder (CaD-VAE), a novel approach for learning causal disentangled representations from interaction data in recommender systems. The approach utilizes structural causal models to generate causal representations that describe the causal relationship between latent factors.
  arXiv  Detail & Related papers  (2023-04-17T00:10:56Z)
• DELTA: Diverse Client Sampling for Fasting Federated Learning [9.45219058010201]
  Partial client participation has been widely adopted in Federated Learning (FL) to reduce the communication burden efficiently. Existing sampling methods are either biased or can be further optimized for faster convergence. We present DELTA, an unbiased sampling scheme designed to alleviate these issues.
  arXiv  Detail & Related papers  (2022-05-27T12:08:23Z)
• Deep Causal Reasoning for Recommendations [47.83224399498504]
  A new trend in recommender system research is to negate the influence of confounders from a causal perspective. We model the recommendation as a multi-cause multi-outcome (MCMO) inference problem. We show that MCMO modeling may lead to high variance due to scarce observations associated with the high-dimensional causal space.
  arXiv  Detail & Related papers  (2022-01-06T15:00:01Z)
• 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)

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.