W. Bruce Croft

Cross Domain Regularization for Neural Ranking Models using Adversarial Learning

2018

Unlike traditional learning to rank models that depend on hand-crafted features, neural representation learning models learn higher level features for the ranking task by training on large datasets. Their ability to learn new features directly …

Neural Query Performance Prediction using Weak Supervision from Multiple Signals

2018

Predicting the performance of a search engine for a given query is a fundamental and challenging task in information retrieval. Accurate performance predictors can be used in various ways, such as triggering an action, choosing …

Revisiting Iterative Relevance Feedback for Document and Passage Retrieval

2018 · arXiv (Cornell University)

As more and more search traffic comes from mobile phones, intelligent assistants, and smart-home devices, new challenges (e.g., limited presentation space) and opportunities come up in information retrieval. Previously, an effective technique, relevance feedback (RF), …

Learning a Joint Search and Recommendation Model from User-Item Interactions

2020

Existing learning to rank models for information retrieval are trained based on explicit or implicit query-document relevance information. In this paper, we study the task of learning a retrieval model based on user-item interactions. Our …

Quary Expansion Using Local and Global Document Analysis

2017 · ACM SIGIR Forum

Automatic query expansion has long been suggested as a technique for dealing with the fundamental issue of word mismatch in information retrieval. A number of approaches to expansion have been studied and, more recently, attention …

A Language Modeling Approach to Information Retrieval

2017 · ACM SIGIR Forum

In today's world, there is no shortage of information. However, for a specific information need, only a small subset of all of the available information will be useful. The field of information retrieval (IR) is …

Relevance-Based Language Models

2017 · ACM SIGIR Forum

We explore the relation between classical probabilistic models of information retrieval and the emerging language modeling approaches. It has long been recognized that the primary obstacle to effective performance of classical models is the need …

Embedding-based Query Language Models

2016

Word embeddings, which are low-dimensional vector representations of vocabulary terms that capture the semantic similarity between them, have recently been shown to achieve impressive performance in many natural language processing tasks. The use of word …

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 …

Neural Ranking Models with Weak Supervision

2017

Despite the impressive improvements achieved by unsupervised deep neural networks in computer vision and NLP tasks, such improvements have not yet been observed in ranking for information retrieval. The reason may be the complexity of …

Relevance-based Word Embedding

2017

Learning a high-dimensional dense representation for vocabulary terms, also known as a word embedding, has recently attracted much attention in natural language processing and information retrieval tasks. The embedding vectors are typically learned based on …

Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources

2017

The Web has accumulated a rich source of information, such as text, image, rating, etc, which represent different aspects of user preferences. However, the heterogeneous nature of this information makes it difficult for recommender systems …

From Neural Re-Ranking to Neural Ranking

2018

The availability of massive data and computing power allowing for effective data driven neural approaches is having a major impact on machine learning and information retrieval research, but these models have a basic problem with …

Towards Conversational Search and Recommendation

2018

Conversational search and recommendation based on user-system dialogs exhibit major differences from conventional search and recommendation tasks in that 1) the user and system can interact for multiple semantically coherent rounds on a task through …

Asking Clarifying Questions in Open-Domain Information-Seeking Conversations

2019

Users often fail to formulate their complex information needs in a single query. As a consequence, they may need to scan multiple result pages or reformulate their queries, which may be a frustrating experience. Alternatively, …