6 citations · 13 across the 8 of their papers we have counts for
8 papers
Accelerating Generative Recommendation via Simple Categorical User Sequence Compression
Qijiong Liu, Lu Fan, Zhongzhou Liu +7
Although generative recommenders demonstrate improved performance with longer sequences, their real-time deployment is hindered by substantial computational costs. To address this…
The Bias is in the Details: An Assessment of Cognitive Bias in LLMs
R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3
As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…
Towards Automated Smart Contract Generation: Evaluation, Benchmarking, and Retrieval-Augmented Repair
Zaoyu Chen, Haoran Qin, Nuo Chen +4
Smart contracts, predominantly written in Solidity and deployed on blockchains such as Ethereum, are immutable after deployment, making functional correctness critical. However, ex…
Evaluating Conversational Recommender Systems via Large Language Models: A User-Centric Framework
Nuo Chen, Quanyu Dai, Xiaoyu Dong +5
Conversational recommender systems (CRSs) integrate both recommendation and dialogue tasks, making their evaluation uniquely challenging. Existing approaches primarily assess CRS p…
AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming in LLM-Based Batch Relevance Assessment
Nuo Chen, Jiqun Liu, Xiaoyu Dong +3
Cognitive biases are systematic deviations in thinking that lead to irrational judgments and problematic decision-making, extensively studied across various fields. Recently, large…
Vector Quantization for Recommender Systems: A Review and Outlook
Qijiong Liu, Xiaoyu Dong, Jiaren Xiao +6
Vector quantization, renowned for its unparalleled feature compression capabilities, has been a prominent topic in signal processing and machine learning research for several decad…
Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability
Nuo Chen, Jiqun Liu, Hanpei Fang +3
This study examines the decoy effect's underexplored influence on user search interactions and methods for measuring information retrieval (IR) systems' vulnerability to this effec…
Decoy Effect in Search Interaction: A Pilot Study
Nuo Chen, Jiqun Liu, Tetsuya Sakai +1
In recent years, the influence of cognitive effects and biases on users' thinking, behaving, and decision-making has garnered increasing attention in the field of interactive infor…