136 citations · 504 across the 28 of their papers we have counts for
6 papers · 1 filter
Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval
Nandan Thakur, Jianmo Ni, Gustavo Hernández Ábrego +3
There has been limited success for dense retrieval models in multilingual retrieval, due to uneven and scarce training data available across multiple languages. Synthetic training…
Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction
Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta +4
Large Language Models (LLMs) have demonstrated exceptional capabilities in generalizing to new tasks in a zero-shot or few-shot manner. However, the extent to which LLMs can compre…
RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses
Honglei Zhuang, Zhen Qin, Rolf Jagerman +6
Recently, substantial progress has been made in text ranking based on pretrained language models such as BERT. However, there are limited studies on how to leverage more powerful s…
Knowledge-aware Neural Collective Matrix Factorization for Cross-domain Recommendation
Li Zhang, Yan Ge, Jun Ma +2
Cross-domain recommendation (CDR) can help customers find more satisfying items in different domains. Existing CDR models mainly use common users or mapping functions as bridges be…
Large Dual Encoders Are Generalizable Retrievers
Jianmo Ni, Chen Qu, Jing Lu +8
It has been shown that dual encoders trained on one domain often fail to generalize to other domains for retrieval tasks. One widespread belief is that the bottleneck layer of a du…
Addressing Marketing Bias in Product Recommendations
Mengting Wan, Jianmo Ni, Rishabh Misra +1
Modern collaborative filtering algorithms seek to provide personalized product recommendations by uncovering patterns in consumer-product interactions. However, these interactions…