Robust Interaction-Based Relevance Modeling for Online e-Commerce Search

• URL: http://arxiv.org/abs/2406.02135v2
• Date: Wed, 25 Sep 2024 14:00:18 GMT
• Title: Robust Interaction-Based Relevance Modeling for Online e-Commerce Search
• Authors: Ben Chen, Huangyu Dai, Xiang Ma, Wen Jiang, Wei Ning,
• Abstract summary: Traditional text-matching techniques fail to capture the nuances of search intent accurately. We introduce a robust interaction-based modeling paradigm to address these shortcomings. To the best of our knowledge, this method is the first interaction-based approach for large e-commerce search relevance calculation.
• Score: 8.499253194630665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semantic relevance calculation is crucial for e-commerce search engines, as it ensures that the items selected closely align with customer intent. Inadequate attention to this aspect can detrimentally affect user experience and engagement. Traditional text-matching techniques are prevalent but often fail to capture the nuances of search intent accurately, so neural networks now have become a preferred solution to processing such complex text matching. Existing methods predominantly employ representation-based architectures, which strike a balance between high traffic capacity and low latency. However, they exhibit significant shortcomings in generalization and robustness when compared to interaction-based architectures. In this work, we introduce a robust interaction-based modeling paradigm to address these shortcomings. It encompasses 1) a dynamic length representation scheme for expedited inference, 2) a professional terms recognition method to identify subjects and core attributes from complex sentence structures, and 3) a contrastive adversarial training protocol to bolster the model's robustness and matching capabilities. Extensive offline evaluations demonstrate the superior robustness and effectiveness of our approach, and online A/B testing confirms its ability to improve relevance in the same exposure position, resulting in more clicks and conversions. To the best of our knowledge, this method is the first interaction-based approach for large e-commerce search relevance calculation. Notably, we have deployed it for the entire search traffic on alibaba.com, the largest B2B e-commerce platform in the world.

Related papers

• GSSF: Generalized Structural Sparse Function for Deep Cross-modal Metric Learning [51.677086019209554]
  We propose a Generalized Structural Sparse to capture powerful relationships across modalities for pair-wise similarity learning. The distance metric delicately encapsulates two formats of diagonal and block-diagonal terms. Experiments on cross-modal and two extra uni-modal retrieval tasks have validated its superiority and flexibility.
  arXiv  Detail & Related papers  (2024-10-20T03:45:50Z)
• Deep Bag-of-Words Model: An Efficient and Interpretable Relevance Architecture for Chinese E-Commerce [31.076432176267335]
  We propose deep Bag-of-Words (DeepBoW) model, an efficient and interpretable relevance architecture for Chinese e-commerce. Our approach proposes to encode the query and the product into the sparse BoW representation, which is a set of word-weight pairs. The relevance score is measured by the accumulation of the matched word between the sparse BoW representation of the query and the product.
  arXiv  Detail & Related papers  (2024-07-12T16:18:05Z)
• Enhancing HOI Detection with Contextual Cues from Large Vision-Language Models [56.257840490146]
  ConCue is a novel approach for improving visual feature extraction in HOI detection. We develop a transformer-based feature extraction module with a multi-tower architecture that integrates contextual cues into both instance and interaction detectors.
  arXiv  Detail & Related papers  (2023-11-26T09:11:32Z)
• Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection [57.13665112065285]
  Human-Object Interaction (HOI) detection is a challenging computer vision task. We present a framework that enhances HOI detection by incorporating structured text knowledge.
  arXiv  Detail & Related papers  (2023-07-25T14:20:52Z)
• Improving Text Matching in E-Commerce Search with A Rationalizable, Intervenable and Fast Entity-Based Relevance Model [78.80174696043021]
  We propose a novel model called the Entity-Based Relevance Model (EBRM) The decomposition allows us to use a Cross-encoder QE relevance module for high accuracy. We also show that pretraining the QE module with auto-generated QE data from user logs can further improve the overall performance.
  arXiv  Detail & Related papers  (2023-07-01T15:44:53Z)
• Contrastive Cross-Modal Knowledge Sharing Pre-training for Vision-Language Representation Learning and Retrieval [12.30468719055037]
  A Contrastive Cross-Modal Knowledge Sharing Pre-training (COOKIE) is developed to grasp the joint text-image representations. The first module is a weight-sharing transformer that builds on the head of the visual and textual encoders. The other one is three specially designed contrastive learning, aiming to share knowledge between different models.
  arXiv  Detail & Related papers  (2022-07-02T04:08:44Z)
• Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval [80.35589927511667]
  Current state-of-the-art approaches to cross-modal retrieval process text and visual input jointly, relying on Transformer-based architectures with cross-attention mechanisms that attend over all words and objects in an image. We propose a novel fine-tuning framework which turns any pretrained text-image multi-modal model into an efficient retrieval model. Our experiments on a series of standard cross-modal retrieval benchmarks in monolingual, multilingual, and zero-shot setups, demonstrate improved accuracy and huge efficiency benefits over the state-of-the-art cross-encoders.
  arXiv  Detail & Related papers  (2021-03-22T15:08:06Z)
• 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)
• Cascaded Human-Object Interaction Recognition [175.60439054047043]
  We introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network. With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding.
  arXiv  Detail & Related papers  (2020-03-09T17:05:04Z)

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.