Deep Uncertainty-Based Explore for Index Construction and Retrieval in Recommendation System
Year: 2024
Type: conference-paper
Abstract: In recommendation systems, the relevance and novelty of the final results are selected through a cascade system of Matching -> Ranking -> Strategy. The matching model serves as the starting point of the pipeline and determines the upper bound of the subsequent stages. Balancing the relevance and novelty of matching results is a crucial step in the design and optimization of recommendation systems, contributing significantly to improving recommendation quality. However, the typical matching algorithms have not simultaneously addressed the relevance and novelty perfectly. One main reason is that deep matching algorithms exhibit significant uncertainty when estimating items in the long tail (e.g., due to insufficient training samples) items.
Authors: Xin Jiang, Kaiqiang Wang, Yinlong Wang, Fengchang Lv, Taiyang Peng, Shuai Yang, Xianteng Wu, Pengye Zhang, Shuo Yuan, Yifan Zeng
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403577848