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The paper introduces SimBert, a later‑fusion model that jointly trains a dual‑tower retrieval component and an auxiliary generation task on the item tower, using a two‑stage pre‑training and fine‑tuning pipeline, which yields a 3.6% relevance boost and reduces bad‑case rates in Xianyu search.
This article presents an in‑depth exploration of an interactive BERT‑based relevance model for health e‑commerce search, detailing the business context, query and product feature extraction, domain‑specific sample generation, model architecture enhancements, offline and online performance gains, and practical deployment through knowledge distillation.
This article presents the QQ Browser search relevance team's experience integrating QQ Browser and Sogou search systems, detailing business overview, relevance system evolution, algorithm architecture, evaluation metrics, deep semantic matching, relevance calibration, and model distillation techniques to improve search relevance performance.
This article presents a comprehensive overview of Alibaba Health's interactive BERT approach for improving relevance in health e‑commerce search, covering business background, model design, domain‑specific data construction, knowledge‑distilled twin‑tower deployment, experimental results, and a detailed Q&A session.
The QQ Browser search relevance team describes a unified, billion‑scale architecture that combines a main and vertical subsystem, a pyramid‑shaped ranking pipeline (recall, coarse, fine), a dedicated GPU‑accelerated relevance service, and hybrid semantic‑matching models (dual‑tower, BERT, matrix fusion) evaluated with offline and online metrics to deliver accurate, fresh, and authoritative results for diverse content and long‑tail queries.
This article describes how Haodf Online improved its medical question‑answer search by analyzing search challenges, adopting semantic similarity models based on pre‑trained language embeddings, designing contrastive training tasks, and evaluating the resulting increase in click‑through rate and user engagement.
The paper describes Xianyu’s new relevance‑matching pipeline—integrating basic, text‑matching, semantic (BERT‑based dual‑tower), multimodal, and click‑graph features and fusing them with a GBDT model—which boosts search DCG@10 by 6.5 %, query satisfaction by 24 % and click interaction by over 20 % while outlining future enhancements for finer attribute matching and richer structured data.
Baidu’s elastic nearline computing architecture inserts an asynchronous, resource‑adaptive layer between online and offline processing, dynamically harvesting idle CPU, GPU and Kunlun XPU capacity to pre‑compute complex recommendation and search policies, enabling peak‑shifting, valley‑filling, higher timeliness and significant business growth at low cost.
This talk by Zhihu search algorithm engineer Shen Zhan details the evolution of text relevance models from TF‑IDF/BM25 to deep semantic matching and BERT, explains the challenges of deploying BERT at scale, and describes practical knowledge‑distillation techniques that improve both online latency and offline storage while maintaining search quality.
This article introduces the background of search relevance, reviews three common matching model types—representation‑based, interaction‑based, and hybrid—describes their architectures such as DSSM, CDSSM, DRMM and DUET, and presents experimental results of these models on DiDi Food’s search system.
The article outlines DiDi Food’s search relevance challenge, defines semantic matching versus traditional keyword methods, describes the recall‑ranking pipeline, and reviews three families of deep matching models—representation‑based (e.g., DSSM), interaction‑based (e.g., DRMM) and hybrid (e.g., DUET)—including experimental results and a recruitment notice.
This article presents a comprehensive overview of Alibaba's brand advertising business model, its technical architecture, and the practical application of NLP algorithms—specifically brand intent recognition and short‑text relevance—detailing model evolution, evaluation results, and future research directions.
The system tackles POI category tagging as a multi‑label classification problem by engineering textual and non‑textual features, mining click‑log and external samples through active learning, and deploying hierarchical and per‑tag deep textCNN models with feature fusion, achieving over 5 % accuracy gain, ten‑fold speedup, and markedly higher precision and coverage that boost map‑search relevance.
This article explains how Elasticsearch computes relevance scores using Lucene's practical scoring formula, term frequency, inverse document frequency, field-length norms, and query normalization, and demonstrates query-time boosting, constant_score, function_score, decay functions, and script_score with practical DSL examples.