Qingyao Ai

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), …

SIGIR 2019 Tutorial on Explainable Recommendation and Search

2019

Explainable recommendation and search attempt to develop models or methods that not only generate high-quality recommendation or search results, but also intuitive explanations of the results for users or system designers, which can help to …

Reinforcement Learning to Rank Using Coarse-grained Rewards

2022 · arXiv (Cornell University)

Learning to rank (LTR) plays a crucial role in various Information Retrieval (IR) tasks. Although supervised LTR methods based on fine-grained relevance labels (e.g., document-level annotations) have achieved significant success, their reliance on costly and …

Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach

2024

Ranking is at the core of many artificial intelligence (AI) applications, including search engines, recommender systems, etc. Modern ranking systems are often constructed with learning-to-rank (LTR) models built from user behavior signals. While previous studies …

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 …

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 …

Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

2018 · Algorithms

Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms—especially the collaborative filtering (CF)- based approaches with shallow or deep models—usually work with various unstructured information sources for recommendation, such as …

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 …