Jiafeng Guo

B-PROP: Bootstrapped Pre-training with Representative Words Prediction for Ad-hoc Retrieval

2021 · arXiv (Cornell University)

Pre-training and fine-tuning have achieved remarkable success in many downstream natural language processing (NLP) tasks. Recently, pre-training methods tailored for information retrieval (IR) have also been explored, and the latest success is the PROP method …

Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off

2024 · Proceedings of the AAAI Conference on Artificial Intelligence

Neural ranking models (NRMs) have shown great success in information retrieval (IR). But their predictions can easily be manipulated using adversarial examples, which are crafted by adding imperceptible perturbations to legitimate documents. This vulnerability raises …

A Comparative Study of Specialized LLMs as Dense Retrievers

2025 · arXiv (Cornell University)

While large language models (LLMs) are increasingly deployed as dense retrievers, the impact of their domain-specific specialization on retrieval effectiveness remains underexplored. This investigation systematically examines how task-specific adaptations in LLMs influence their retrieval capabilities, …

Learning Hierarchical Representation Model for NextBasket Recommendation

2015

Next basket recommendation is a crucial task in market basket analysis. Given a user's purchase history, usually a sequence of transaction data, one attempts to build a recommender that can predict the next few items …

A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations

2016 · Proceedings of the AAAI Conference on Artificial Intelligence

Matching natural language sentences is central for many applications such as information retrieval and question answering. Existing deep models rely on a single sentence representation or multiple granularity representations for matching. However, such methods cannot …

Modeling Document Novelty with Neural Tensor Network for Search Result Diversification

2016

Search result diversification has attracted considerable attention as a means to tackle the ambiguous or multi-faceted information needs of users. One of the key problems in search result diversification is novelty, that is, how to …

A Deep Relevance Matching Model for Ad-hoc Retrieval

2016

In recent years, deep neural networks have led to exciting breakthroughs in speech recognition, computer vision, and natural language processing (NLP) tasks. However, there have been few positive results of deep models on ad-hoc retrieval …

Reinforcement Learning to Rank with Markov Decision Process

2017

One of the central issues in learning to rank for information retrieval is to develop algorithms that construct ranking models by directly optimizing evaluation measures such as normalized discounted cumulative gain~(ND CG). Existing methods usually …

A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations

2015 · arXiv (Cornell University)

Matching natural language sentences is central for many applications such as information retrieval and question answering. Existing deep models rely on a single sentence representation or multiple granularity representations for matching. However, such methods cannot …