From Semantic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Search

• URL: http://arxiv.org/abs/2103.12982v1
• Date: Wed, 24 Mar 2021 04:37:32 GMT
• Title: From Semantic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Search
• Authors: Rui Li, Yunjiang Jiang, Wenyun Yang, Guoyu Tang, Songlin Wang, Chaoyi Ma, Wei He, Xi Xiong, Yun Xiao, Eric Yihong Zhao
• Abstract summary: We introduce deep learning models to the two most important stages in product search at JD.com. Specifically, we outline the design of a deep learning system that retrieves semantically relevant items to a query within milliseconds, and a pairwise deep re-ranking system, which learns subtle user preferences.
• Score: 11.459020190110019
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce deep learning models to the two most important stages in product search at JD.com, one of the largest e-commerce platforms in the world. Specifically, we outline the design of a deep learning system that retrieves semantically relevant items to a query within milliseconds, and a pairwise deep re-ranking system, which learns subtle user preferences. Compared to traditional search systems, the proposed approaches are better at semantic retrieval and personalized ranking, achieving significant improvements.

Related papers

• Semantic Ads Retrieval at Walmart eCommerce with Language Models Progressively Trained on Multiple Knowledge Domains [6.1008328784394]
  We present an end-to-end solution tailored to optimize the ads retrieval system on Walmart.com. Our approach is to pretrain the BERT-like classification model with product category information. It enhances the search relevance metric by up to 16% compared to a baseline DSSM-based model.
  arXiv  Detail & Related papers  (2025-02-13T09:01:34Z)
• Semantic Retrieval at Walmart [41.15739523513742]
  We present a hybrid system for e-commerce search deployed at Walmart that combines traditional inverted index and embedding-based neural retrieval. Our system significantly improved the relevance of the search engine, measured by both offline and online evaluations.
  arXiv  Detail & Related papers  (2024-12-05T22:10:58Z)
• A Comprehensive Survey on Retrieval Methods in Recommender Systems [32.1847120460637]
  This survey explores the critical yet often overlooked retrieval stage of recommender systems. To achieve precise and efficient personalized retrieval, we summarize existing work in three key areas. We highlight current industrial applications through a case study on retrieval practices at a specific company.
  arXiv  Detail & Related papers  (2024-07-11T07:09:59Z)
• A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning [51.7818820745221]
  Underwater image enhancement (UIE) presents a significant challenge within computer vision research. Despite the development of numerous UIE algorithms, a thorough and systematic review is still absent.
  arXiv  Detail & Related papers  (2024-05-30T04:46:40Z)
• Generative Multi-Modal Knowledge Retrieval with Large Language Models [75.70313858231833]
  We propose an innovative end-to-end generative framework for multi-modal knowledge retrieval. Our framework takes advantage of the fact that large language models (LLMs) can effectively serve as virtual knowledge bases. We demonstrate significant improvements ranging from 3.0% to 14.6% across all evaluation metrics when compared to strong baselines.
  arXiv  Detail & Related papers  (2024-01-16T08:44:29Z)
• Recommender Systems with Generative Retrieval [58.454606442670034]
  We propose a novel generative retrieval approach, where the retrieval model autoregressively decodes the identifiers of the target candidates. To that end, we create semantically meaningful of codewords to serve as a Semantic ID for each item. We show that recommender systems trained with the proposed paradigm significantly outperform the current SOTA models on various datasets.
  arXiv  Detail & Related papers  (2023-05-08T21:48:17Z)
• Que2Engage: Embedding-based Retrieval for Relevant and Engaging Products at Facebook Marketplace [15.054431410052851]
  We present Que2Engage, a search EBR system built towards bridging the gap between retrieval and ranking for end-to-end optimizations. We show the effectiveness of our approach via a multitask evaluation framework and thorough baseline comparisons and ablation studies.
  arXiv  Detail & Related papers  (2023-02-21T23:10:16Z)
• Sequential Search with Off-Policy Reinforcement Learning [48.88165680363482]
  We propose a highly scalable hybrid learning model that consists of an RNN learning framework and an attention model. As a novel optimization step, we fit multiple short user sequences in a single RNN pass within a training batch, by solving a greedy knapsack problem on the fly. We also explore the use of off-policy reinforcement learning in multi-session personalized search ranking.
  arXiv  Detail & Related papers  (2022-02-01T06:52:40Z)
• Exposing Query Identification for Search Transparency [69.06545074617685]
  We explore the feasibility of approximate exposing query identification (EQI) as a retrieval task by reversing the role of queries and documents in two classes of search systems. We derive an evaluation metric to measure the quality of a ranking of exposing queries, as well as conducting an empirical analysis focusing on various practical aspects of approximate EQI.
  arXiv  Detail & Related papers  (2021-10-14T20:19:27Z)
• Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning [113.05118113697111]
  Few-shot learning aims to adapt knowledge learned from previous tasks to novel tasks with only a limited amount of labeled data. Research literature on few-shot learning exhibits great diversity, while different algorithms often excel at different few-shot learning scenarios. We present Meta Navigator, a framework that attempts to solve the limitation in few-shot learning by seeking a higher-level strategy.
  arXiv  Detail & Related papers  (2021-09-13T07:20:01Z)
• Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval [30.95373255143698]
  We propose a new Zero-Shot Heterogeneous Transfer Learning framework that transfers learned knowledge from the recommender system component to improve the search component of a content platform. We conduct online and offline experiments on one of the world's largest search and recommender systems from Google, and present the results and lessons learned.
  arXiv  Detail & Related papers  (2020-08-07T01:22:56Z)

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.