PRECTR: A Synergistic Framework for Integrating Personalized Search Relevance Matching and CTR Prediction

• URL: http://arxiv.org/abs/2503.18395v2
• Date: Wed, 26 Mar 2025 14:38:27 GMT
• Title: PRECTR: A Synergistic Framework for Integrating Personalized Search Relevance Matching and CTR Prediction
• Authors: Rong Chen, Shuzhi Cao, Ailong He, Shuguang Han, Jufeng Chen,
• Abstract summary: Two primary tasks in the search recommendation system are search relevance matching and click-through rate (CTR) prediction.<n>We propose a unified Personalized Search RElevance Matching and CTR Prediction Fusion Model(PRECTR)<n>Specifically, PRECTR integrates the CTR prediction and search relevance matching into one framework to enhance the interaction and consistency of the two modules.
• Score: 3.753643743882433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The two primary tasks in the search recommendation system are search relevance matching and click-through rate (CTR) prediction -- the former focuses on seeking relevant items for user queries whereas the latter forecasts which item may better match user interest. Prior research typically develops two models to predict the CTR and search relevance separately, then ranking candidate items based on the fusion of the two outputs. However, such a divide-and-conquer paradigm creates the inconsistency between different models. Meanwhile, the search relevance model mainly concentrates on the degree of objective text matching while neglecting personalized differences among different users, leading to restricted model performance. To tackle these issues, we propose a unified Personalized Search RElevance Matching and CTR Prediction Fusion Model(PRECTR). Specifically, based on the conditional probability fusion mechanism, PRECTR integrates the CTR prediction and search relevance matching into one framework to enhance the interaction and consistency of the two modules. However, directly optimizing CTR binary classification loss may bring challenges to the fusion model's convergence and indefinitely promote the exposure of items with high CTR, regardless of their search relevance. Hence, we further introduce two-stage training and semantic consistency regularization to accelerate the model's convergence and restrain the recommendation of irrelevant items. Finally, acknowledging that different users may have varied relevance preferences, we assessed current users' relevance preferences by analyzing past users' preferences for similar queries and tailored incentives for different candidate items accordingly. Extensive experimental results on our production dataset and online A/B testing demonstrate the effectiveness and superiority of our proposed PRECTR method.

Related papers

• Behavior Modeling Space Reconstruction for E-Commerce Search [32.46624795081706]
  Search systems model user behaviors by combining user preference and query item relevance statically, often through a fixed logical 'and' relationship.<n>This paper reexamines existing approaches through a unified lens using both causal graphs and Venn diagrams.<n>To surmount these challenges, our research introduces a novel framework, which enhances search accuracy through two components to reconstruct the behavior modeling space.
  arXiv  Detail & Related papers  (2025-01-30T09:17:04Z)
• CRM: Retrieval Model with Controllable Condition [23.936944737868465]
  Controllable Retrieval Model integrates regression information as conditional features into the two-tower retrieval paradigm.<n>We validate the effectiveness of CRM through real-world A/B testing and demonstrate its successful deployment in Kuaishou short-video recommendation system.
  arXiv  Detail & Related papers  (2024-12-18T13:37:36Z)
• Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction [68.90783662117936]
  Click-through Rate (CTR) prediction is crucial for online personalization platforms.<n>Recent advancements have shown that modeling rich user behaviors can significantly improve the performance of CTR prediction.<n>We propose Multi-granularity Interest Retrieval and Refinement Network (MIRRN)
  arXiv  Detail & Related papers  (2024-11-22T15:29:05Z)
• 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)
• Long-Sequence Recommendation Models Need Decoupled Embeddings [49.410906935283585]
  We identify and characterize a neglected deficiency in existing long-sequence recommendation models. A single set of embeddings struggles with learning both attention and representation, leading to interference between these two processes. We propose the Decoupled Attention and Representation Embeddings (DARE) model, where two distinct embedding tables are learned separately to fully decouple attention and representation.
  arXiv  Detail & Related papers  (2024-10-03T15:45:15Z)
• Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization [75.1240295759264]
  We propose an effective framework for Bridging and Modeling Correlations in pairwise data, named BMC.<n>We increase the consistency and informativeness of the pairwise preference signals through targeted modifications.<n>We identify that DPO alone is insufficient to model these correlations and capture nuanced variations.
  arXiv  Detail & Related papers  (2024-08-14T11:29:47Z)
• FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction [49.510163437116645]
  Click-through rate (CTR) prediction plays as a core function module in personalized online services. Traditional ID-based models for CTR prediction take as inputs the one-hot encoded ID features of tabular modality. Pretrained Language Models(PLMs) has given rise to another paradigm, which takes as inputs the sentences of textual modality. We propose to conduct Fine-grained feature-level ALignment between ID-based Models and Pretrained Language Models(FLIP) for CTR prediction.
  arXiv  Detail & Related papers  (2023-10-30T11:25:03Z)
• Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in eCommerce [51.211924408864355]
  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.
  arXiv  Detail & Related papers  (2023-07-29T19:41:16Z)
• Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction [97.99938802797377]
  Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in recommender systems. Recent deep learning models with the ability to automatically extract the user interest from his/her behaviors have achieved great success. We propose a novel approach under the framework of the wrapper method, which is named Meta-Wrapper.
  arXiv  Detail & Related papers  (2022-06-28T03:28:15Z)
• Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction [76.98616102965023]
  Cross domain recommender system constitutes a powerful method to tackle the cold-start and sparsity problem. We propose a novel approach to cross-domain sequential recommendations based on the dual learning mechanism.
  arXiv  Detail & Related papers  (2021-06-05T01:21:21Z)
• Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search [29.881612817309716]
  We design an end-to-end First-and-Second-order Relevance prediction model for e-commerce item relevance. We introduce external knowledge generated from BERT to refine the network of user behaviors. Results of offline experiments showed that the new model significantly improved the prediction accuracy in terms of human relevance judgment.
  arXiv  Detail & Related papers  (2021-01-13T03:12:53Z)

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.