Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in eCommerce

• URL: http://arxiv.org/abs/2307.16060v1
• Date: Sat, 29 Jul 2023 19:41:16 GMT
• Title: Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in eCommerce
• Authors: Yibo Wang, Yanbing Xue, Bo Liu, Musen Wen, Wenting Zhao, Stephen Guo, Philip S. Yu
• Abstract summary: We propose two position-bias-free prediction models: Position-Aware Click-Conversion (PACC) and PACC via Position Embedding (PACC-PE) Experiments on the E-commerce sponsored product search dataset show that our proposed models have better ranking effectiveness and can greatly alleviate position bias in both CTR and CVR prediction.
• Score: 51.211924408864355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Position bias, the phenomenon whereby users tend to focus on higher-ranked items of the search result list regardless of the actual relevance to queries, is prevailing in many ranking systems. Position bias in training data biases the ranking model, leading to increasingly unfair item rankings, click-through-rate (CTR), and conversion rate (CVR) predictions. To jointly mitigate position bias in both item CTR and CVR prediction, we propose two position-bias-free CTR and CVR prediction models: Position-Aware Click-Conversion (PACC) and PACC via Position Embedding (PACC-PE). PACC is built upon probability decomposition and models position information as a probability. PACC-PE utilizes neural networks to model product-specific position information as embedding. Experiments on the E-commerce sponsored product search dataset show that our proposed models have better ranking effectiveness and can greatly alleviate position bias in both CTR and CVR prediction.

Related papers

• Unbiased Learning to Rank with Query-Level Click Propensity Estimation: Beyond Pointwise Observation and Relevance [74.43264459255121]
  In real-world scenarios, users often click only one or two results after examining multiple relevant options. We propose a query-level click propensity model to capture the probability that users will click on different result lists. Our method introduces a Dual Inverse Propensity Weighting mechanism to address both relevance saturation and position bias.
  arXiv  Detail & Related papers  (2025-02-17T03:55:51Z)
• An accuracy improving method for advertising click through rate prediction based on enhanced xDeepFM model [0.0]
  This paper proposes an improved CTR prediction model based on the xDeepFM architecture. By integrating a multi-head attention mechanism, the model can simultaneously focus on different aspects of feature interactions. Experimental results on the Criteo dataset demonstrate that the proposed model outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-21T03:21:29Z)
• Eliminating Position Bias of Language Models: A Mechanistic Approach [119.34143323054143]
  Position bias has proven to be a prevalent issue of modern language models (LMs) Our mechanistic analysis attributes the position bias to two components employed in nearly all state-of-the-art LMs: causal attention and relative positional encodings. By eliminating position bias, models achieve better performance and reliability in downstream tasks, including LM-as-a-judge, retrieval-augmented QA, molecule generation, and math reasoning.
  arXiv  Detail & Related papers  (2024-07-01T09:06:57Z)
• Helen: Optimizing CTR Prediction Models with Frequency-wise Hessian Eigenvalue Regularization [22.964109377128523]
  Click-Through Rate (CTR) prediction holds paramount significance in online advertising and recommendation scenarios. Despite the proliferation of recent CTR prediction models, the improvements in performance have remained limited.
  arXiv  Detail & Related papers  (2024-02-23T15:00:46Z)
• Rank-DETR for High Quality Object Detection [52.82810762221516]
  A highly performant object detector requires accurate ranking for the bounding box predictions. In this work, we introduce a simple and highly performant DETR-based object detector by proposing a series of rank-oriented designs.
  arXiv  Detail & Related papers  (2023-10-13T04:48:32Z)
• CSPM: A Contrastive Spatiotemporal Preference Model for CTR Prediction in On-Demand Food Delivery Services [17.46228008447778]
  This paper introduces Contrasttemporal representation learning (CSRL),temporal representation extractor (CSRPE), andtemporal information filter (StIF) StIF incorporates SAR into a gating network to automatically capture important features with latenttemporal effects. CSPM has been successfully deployed in Alibaba's online OFD platform Ele.me, resulting in a 0.88% lift in CTR, which has substantial business implications.
  arXiv  Detail & Related papers  (2023-08-10T19:53:30Z)
• An Offline Metric for the Debiasedness of Click Models [52.25681483524383]
  Click models are a common method for extracting information from user clicks. Recent work shows that the current evaluation practices in the community fail to guarantee that a well-performing click model generalizes well to downstream tasks. We introduce the concept of debiasedness in click modeling and derive a metric for measuring it.
  arXiv  Detail & Related papers  (2023-04-19T10:59:34Z)
• Cross Pairwise Ranking for Unbiased Item Recommendation [57.71258289870123]
  We develop a new learning paradigm named Cross Pairwise Ranking (CPR) CPR achieves unbiased recommendation without knowing the exposure mechanism. We prove in theory that this way offsets the influence of user/item propensity on the learning.
  arXiv  Detail & Related papers  (2022-04-26T09:20:27Z)
• Rethinking Position Bias Modeling with Knowledge Distillation for CTR Prediction [8.414183573280779]
  This work proposes a knowledge distillation framework to alleviate the impact of position bias and leverage position information to improve CTR prediction. The proposed method has been deployed in the real world online ads systems, serving main traffic on one of the world's largest e-commercial platforms.
  arXiv  Detail & Related papers  (2022-04-01T07:58:38Z)
• Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model [11.101369123145588]
  Off-policy evaluation for ranking policies enables performance estimation of new ranking policies using only logged data. Previous studies introduce some assumptions on user behavior to make the item space tractable. We propose the Cascade Doubly Robust estimator, which assumes that a user interacts with items sequentially from the top position in a ranking.
  arXiv  Detail & Related papers  (2022-02-03T12:42:33Z)

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.