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20162023
most citedStructure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data

2 citations · 3 across the 5 of their papers we have counts for

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cs.IR2023

Text Matching Improves Sequential Recommendation by Reducing Popularity Biases

Zhenghao Liu, Sen Mei, Chenyan Xiong +5

This paper proposes Text mAtching based SequenTial rEcommendation model (TASTE), which maps items and users in an embedding space and recommends items by matching their text repres…

cs.IR2023★ 2 cited

Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data

Xinze Li, Zhenghao Liu, Chenyan Xiong +4

This paper presents Structure Aware Dense Retrieval (SANTA) model, which encodes user queries and structured data in one universal embedding space for retrieving structured data. S…