8 citations · 11 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
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…
Rethinking Dense Retrieval's Few-Shot Ability
Si Sun, Yida Lu, Shi Yu +6
Few-shot dense retrieval (DR) aims to effectively generalize to novel search scenarios by learning a few samples. Despite its importance, there is little study on specialized datas…
RS2G: Data-Driven Scene-Graph Extraction and Embedding for Robust Autonomous Perception and Scenario Understanding
Junyao Wang, Arnav Vaibhav Malawade, Junhong Zhou +2
Effectively capturing intricate interactions among road users is of critical importance to achieving safe navigation for autonomous vehicles. While graph learning (GL) has emerged…
CFG2VEC: Hierarchical Graph Neural Network for Cross-Architectural Software Reverse Engineering
Shih-Yuan Yu, Yonatan Gizachew Achamyeleh, Chonghan Wang +3
Mission-critical embedded software is critical to our society's infrastructure but can be subject to new security vulnerabilities as technology advances. When security issues arise…