Deep Retrieval: Learning Efficient Structures for Large-Scale Recommendation Systems
Update: 2024-08-31
Description
The paper introduces a novel approach called Deep Retrieval (DR) which learns a retrievable structure directly from user-item interaction data in large-scale recommendation systems. Unlike traditional vector-based models, DR captures complex user-item relationships by creating a structure that reflects user preferences more effectively.
Engineers and specialists can benefit from the paper by understanding how DR revolutionizes large-scale recommendation systems through its innovative approach of learning efficient structures directly from user-item interactions. By adopting a path-based mechanism and utilizing multi-path designs, DR can provide accurate recommendations comparable to computationally expensive methods while remaining more efficient. The ability of DR to handle diverse preferences, promote less popular content, and improve user engagement highlights its potential to reshape recommendation systems for better performance and inclusivity.
Read full paper: https://arxiv.org/abs/2007.07203
Tags: Machine Learning, Recommendation Systems, Information Retrieval, Deep Learning
Engineers and specialists can benefit from the paper by understanding how DR revolutionizes large-scale recommendation systems through its innovative approach of learning efficient structures directly from user-item interactions. By adopting a path-based mechanism and utilizing multi-path designs, DR can provide accurate recommendations comparable to computationally expensive methods while remaining more efficient. The ability of DR to handle diverse preferences, promote less popular content, and improve user engagement highlights its potential to reshape recommendation systems for better performance and inclusivity.
Read full paper: https://arxiv.org/abs/2007.07203
Tags: Machine Learning, Recommendation Systems, Information Retrieval, Deep Learning
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