Beam Search Aware Training for Optimal Tree-Based Retrieval Models

This article presents a comprehensive study of tree-based deep models for large-scale matching, introduces the theoretical framework of optimal tree models, proposes a Beam Search aware training algorithm (BSAT/OTM) to address training-test mismatch, and demonstrates significant recall improvements on Amazon Books and UserBehavior datasets.

Beam SearchDeep Learninglarge-scale matching

0 likes · 23 min read

Beam Search Aware Training for Optimal Tree-Based Retrieval Models

DataFunTalk

Dec 24, 2019 · Artificial Intelligence

Evolution of Recall Models in Recommendation Systems: From Collaborative Filtering to Deep Learning and Tree‑Based Retrieval

This article surveys the development of recall modules in large‑scale recommendation systems, covering traditional item‑based collaborative filtering, single‑embedding DNN and dual‑tower approaches, multi‑interest capsule networks, graph‑based embeddings, long‑short term interest modeling, and the tree‑structured TDM framework for efficient deep matching.

Deep LearningRecommendation Systemsgraph embedding

0 likes · 14 min read

Evolution of Recall Models in Recommendation Systems: From Collaborative Filtering to Deep Learning and Tree‑Based Retrieval

Alibaba Cloud Developer

Mar 28, 2018 · Artificial Intelligence

How Tree‑Based Deep Match Revolutionizes Large‑Scale Recommendation Systems

This article introduces the Tree‑based Deep Match (TDM) framework, which uses a novel max‑heap tree structure to enable efficient, hierarchical retrieval over massive candidate sets, allowing any advanced deep learning model to improve matching accuracy, recall, and novelty in industrial recommendation systems.

Deep Learninglarge-scale recommendationmachine learning

0 likes · 27 min read

How Tree‑Based Deep Match Revolutionizes Large‑Scale Recommendation Systems