Unified Generative & Dense Retrieval for Query Rewriting in Sponsored Search

• URL: http://arxiv.org/abs/2209.05861v2
• Date: Sat, 3 Jun 2023 12:19:43 GMT
• Title: Unified Generative & Dense Retrieval for Query Rewriting in Sponsored Search
• Authors: Akash Kumar Mohankumar, Bhargav Dodla, Gururaj K, Amit Singh
• Abstract summary: We compare two paradigms for online query rewriting: Generative (NLG) and Dense Retrieval (DR) methods. We propose CLOVER-Unity, a novel approach that unifies generative and dense retrieval methods in one single model.
• Score: 6.181557214852772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sponsored search is a key revenue source for search engines, where advertisers bid on keywords to target users or search queries of interest. However, finding relevant keywords for a given query is challenging due to the large and dynamic keyword space, ambiguous user/advertiser intents, and diverse possible topics and languages. In this work, we present a comprehensive comparison between two paradigms for online query rewriting: Generative (NLG) and Dense Retrieval (DR) methods. We observe that both methods offer complementary benefits that are additive. As a result, we show that around 40% of the high-quality keywords retrieved by the two approaches are unique and not retrieved by the other. To leverage the strengths of both methods, we propose CLOVER-Unity, a novel approach that unifies generative and dense retrieval methods in one single model. Through offline experiments, we show that the NLG and DR components of CLOVER-Unity consistently outperform individually trained NLG and DR models on public and internal benchmarks. Furthermore, we show that CLOVER-Unity achieves 9.8% higher good keyword density than the ensemble of two separate DR and NLG models while reducing computational costs by almost half. We conduct extensive online A/B experiments on Microsoft Bing in 140+ countries and achieve improved user engagement, with an average increase in total clicks by 0.89% and increased revenue by 1.27%. We also share our practical lessons and optimization tricks for deploying such unified models in production.

Related papers

• Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization [21.115495457454365]
  This paper investigates the design of a unified search engine to serve multiple retrieval-augmented generation (RAG) agents. We introduce an iterative approach where the search engine generates retrieval results for these RAG agents and gathers feedback on the quality of the retrieved documents during an offline phase. We adapt this approach to an online setting, allowing the search engine to refine its behavior based on real-time individual agents feedback.
  arXiv  Detail & Related papers  (2024-10-13T17:53:50Z)
• Contrastive Transformer Learning with Proximity Data Generation for Text-Based Person Search [60.626459715780605]
  Given a descriptive text query, text-based person search aims to retrieve the best-matched target person from an image gallery. Such a cross-modal retrieval task is quite challenging due to significant modality gap, fine-grained differences and insufficiency of annotated data. In this paper, we propose a simple yet effective dual Transformer model for text-based person search.
  arXiv  Detail & Related papers  (2023-11-15T16:26:49Z)
• Unified Embedding Based Personalized Retrieval in Etsy Search [0.206242362470764]
  We propose learning a unified embedding model incorporating graph, transformer and term-based embeddings end to end. Our personalized retrieval model significantly improves the overall search experience, as measured by a 5.58% increase in search purchase rate and a 2.63% increase in site-wide conversion rate.
  arXiv  Detail & Related papers  (2023-06-07T23:24:50Z)
• LEAPS: End-to-End One-Step Person Search With Learnable Proposals [50.39493100627476]
  We propose an end-to-end one-step person search approach with learnable proposals, named LEAPS. Given a set of sparse and learnable proposals, LEAPS employs a dynamic person search head to directly perform person detection and corresponding re-id feature generation without non-maximum suppression post-processing.
  arXiv  Detail & Related papers  (2023-03-21T13:59:32Z)
• Enriching Relation Extraction with OpenIE [70.52564277675056]
  Relation extraction (RE) is a sub-discipline of information extraction (IE) In this work, we explore how recent approaches for open information extraction (OpenIE) may help to improve the task of RE. Our experiments over two annotated corpora, KnowledgeNet and FewRel, demonstrate the improved accuracy of our enriched models.
  arXiv  Detail & Related papers  (2022-12-19T11:26:23Z)
• Query Expansion Using Contextual Clue Sampling with Language Models [69.51976926838232]
  We propose a combination of an effective filtering strategy and fusion of the retrieved documents based on the generation probability of each context. Our lexical matching based approach achieves a similar top-5/top-20 retrieval accuracy and higher top-100 accuracy compared with the well-established dense retrieval model DPR. For end-to-end QA, the reader model also benefits from our method and achieves the highest Exact-Match score against several competitive baselines.
  arXiv  Detail & Related papers  (2022-10-13T15:18:04Z)
• Multi-Objective Personalized Product Retrieval in Taobao Search [27.994166796745496]
  We propose a novel Multi-Objective Personalized Product Retrieval (MOPPR) model with four hierarchical optimization objectives: relevance, exposure, click and purchase. MOPPR achieves 0.96% transaction and 1.29% GMV improvements in a 28-day online A/B test. Since the Double-11 shopping festival of 2021, MOPPR has been fully deployed in mobile Taobao search, replacing the previous MGDSPR.
  arXiv  Detail & Related papers  (2022-10-09T05:18:42Z)
• Advertising Media and Target Audience Optimization via High-dimensional Bandits [2.5137859989323537]
  We present a data-driven algorithm that advertisers can use to automate their digital ad-campaigns at online publishers. The algorithm enables the advertiser to search across available target audiences and ad-media to find the best possible combination for its campaign via online experimentation.
  arXiv  Detail & Related papers  (2022-09-17T21:00:53Z)
• Diversity driven Query Rewriting in Search Advertising [1.5289756643078838]
  generative retrieval models have been shown to be effective at the task of generating such query rewrites. We introduce CLOVER, a framework to generate both high-quality and diverse rewrites. We empirically show the effectiveness of our proposed approach through offline experiments on search queries across geographies spanning three major languages.
  arXiv  Detail & Related papers  (2021-06-07T17:30:45Z)
• One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking [97.60915598958968]
  We propose a one-shot neural ensemble architecture search (NEAS) solution that addresses the two challenges. For the first challenge, we introduce a novel diversity-based metric to guide search space shrinking. For the second challenge, we enable a new search dimension to learn layer sharing among different models for efficiency purposes.
  arXiv  Detail & Related papers  (2021-04-01T16:29:49Z)
• Diverse Knowledge Distillation for End-to-End Person Search [81.4926655119318]
  Person search aims to localize and identify a specific person from a gallery of images. Recent methods can be categorized into two groups, i.e., two-step and end-to-end approaches. We propose a simple yet strong end-to-end network with diverse knowledge distillation to break the bottleneck.
  arXiv  Detail & Related papers  (2020-12-21T09:04:27Z)

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.