vector recall | BestHub

DaTaobao Tech

Jun 19, 2024 · Product Management

Multi‑Interest Vector Recall and PDN Models for Large‑Asset Recommendation in Alibaba Auction

Alibaba Auction improves large‑asset recommendation by deploying the multi‑interest vector recall model MIND and the two‑hop PDN model, adapting features and time weighting for unique, high‑value items, using hard‑negative sampling and combined rule‑based and vector similarity, which boosts conversion metrics while revealing filter‑bubble concerns.

PDNdeep learninge-commerce

0 likes · 13 min read

Multi‑Interest Vector Recall and PDN Models for Large‑Asset Recommendation in Alibaba Auction

DataFunTalk

Jan 25, 2023 · Artificial Intelligence

Optimizing Vector Recall for Feizhu's Homepage "You May Like" Recommendation Feeds

This article presents a comprehensive overview of the background, current multi‑path recall methods, and a series of practical optimizations—including dual‑tower models, enhanced vectors, an unbiased IPW‑based framework, and a travel‑state‑aware deep recall model—applied to Feizhu's homepage recommendation system, with both offline and online experimental results demonstrating click‑through rate improvements.

Bias MitigationRecommendation systemsdual-tower model

0 likes · 17 min read

Optimizing Vector Recall for Feizhu's Homepage "You May Like" Recommendation Feeds

Youzan Coder

Oct 24, 2022 · Artificial Intelligence

Knowledge Base Retrieval Matching: Algorithm and Engineering Service Practice

The article outlines a comprehensive knowledge‑base retrieval matching solution—combining PageRank‑enhanced DSL rewriting, keyword and dual‑tower vector recall, contrastive fine‑ranking, and optimized vector‑based ranking—implemented via offline DP training and Sunfish online inference on Milvus, with applications in enterprise search and recommendations and future plans for graph‑neural embeddings.

InfoNCEMilvusNLP

0 likes · 12 min read

Knowledge Base Retrieval Matching: Algorithm and Engineering Service Practice

DataFunTalk

Sep 19, 2021 · Artificial Intelligence

Second‑hand Housing Recommendation System: Business Background, Vector Recall, Multi‑objective Optimization and Future Plans

This article presents the end‑to‑end practice of a second‑hand housing recommendation system at 58.com and Anjuke, covering business background, embedding‑based vector recall, multi‑objective ranking methods such as ESMM and MMOE, experimental results, and future development directions.

ESMMReal Estateembedding

0 likes · 14 min read

Second‑hand Housing Recommendation System: Business Background, Vector Recall, Multi‑objective Optimization and Future Plans

DataFunTalk

Feb 25, 2021 · Artificial Intelligence

Applying Graph Embedding and Vector Recall for Personalized Recommendation in a UGC Community

This article describes how a UGC app tackled user and content cold‑start problems by introducing a personalized vector‑recall pipeline based on network representation learning and multimodal embeddings, detailing graph construction, GraphSAGE and GAT implementations, offline experiments, A/B test results, and future directions.

GNNgraph embeddingmultimodal

0 likes · 14 min read

Applying Graph Embedding and Vector Recall for Personalized Recommendation in a UGC Community

Jike Tech Team

Jul 15, 2020 · Artificial Intelligence

How Embedding-Based Recall Boosted Interaction by 33% in a Live Feed

This article details how Jike's recommendation team upgraded from Spark to TensorFlow, introduced a twin‑tower embedding model for recall, deployed it with TensorFlow Serving and Elasticsearch, and achieved a 33.75% lift in user interaction on the dynamic square.

ElasticsearchTensorFlow Servingdeep learning

0 likes · 9 min read