325 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Alibaba Group (United States)US30 papers
- Microsoft (United States)US20 papers
- University of OxfordGB19 papers
- Alibaba Group (China)CN14 papers
- Goddard Space Flight CenterUS14 papers
- Massachusetts Institute of TechnologyUS14 papers
- Search for Extraterrestrial IntelligenceUS14 papers
- Eötvös Loránd UniversityHU13 papers
- HUN-REN Research Centre for Astronomy and Earth SciencesHU13 papers
- Konkoly ObservatoryHU13 papers
- University of SzegedHU13 papers
- Tencent (China)CN12 papers
15 papers · 1 filter
Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation
Jiang Zhang, Sumit Kumar, Wei Chang +7
The task of item-to-item (I2I) retrieval is to identify a set of relevant and highly engaging items based on a given trigger item. It is a crucial component in modern recommendatio…
Finding Interest Needle in Popularity Haystack: Improving Retrieval by Modeling Item Exposure
Rahul Agarwal, Amit Jaspal, Saurabh Gupta +1
Recommender systems operate in closed feedback loops, where user interactions reinforce popularity bias, leading to over-recommendation of already popular items while under-exposin…
HyperZero: A Customized End-to-End Auto-Tuning System for Recommendation with Hourly Feedback
Xufeng Cai, Ziwei Guan, Lei Yuan +11
Modern recommendation systems can be broadly divided into two key stages: the ranking stage, where the system predicts various user engagements (e.g., click-through rate, like rate…
Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems
Xiaohui Chen, Jiankai Sun, Taiqing Wang +3
Data subsampling is widely used to speed up the training of large-scale recommendation systems. Most subsampling methods are model-based and often require a pre-trained pilot model…
Human Preferences as Dueling Bandits
Xinyi Yan, Chengxi Luo, Charles L. A. Clarke +3
The dramatic improvements in core information retrieval tasks engendered by neural rankers create a need for novel evaluation methods. If every ranker returns highly relevant items…
Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning
Yang Deng, Yaliang Li, Fei Sun +2
Conversational recommender systems (CRS) enable the traditional recommender systems to explicitly acquire user preferences towards items and attributes through interactive conversa…
MS MARCO: Benchmarking Ranking Models in the Large-Data Regime
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +2
Evaluation efforts such as TREC, CLEF, NTCIR and FIRE, alongside public leaderboard such as MS MARCO, are intended to encourage research and track our progress, addressing big ques…
TREC Deep Learning Track: Reusable Test Collections in the Large Data Regime
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +3
The TREC Deep Learning (DL) Track studies ad hoc search in the large data regime, meaning that a large set of human-labeled training data is available. Results so far indicate that…
Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence
Bhaskar Mitra, Sebastian Hofstatter, Hamed Zamani +1
The Transformer-Kernel (TK) model has demonstrated strong reranking performance on the TREC Deep Learning benchmark -- and can be considered to be an efficient (but slightly less e…
MIMICS: A Large-Scale Data Collection for Search Clarification
Hamed Zamani, Gord Lueck, Everest Chen +3
Search clarification has recently attracted much attention due to its applications in search engines. It has also been recognized as a major component in conversational information…
Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search
Helia Hashemi, Hamed Zamani, W. Bruce Croft
Asking clarifying questions in response to ambiguous or faceted queries has been recognized as a useful technique for various information retrieval systems, especially conversation…
Sequential Recommendation with Self-Attentive Multi-Adversarial Network
Ruiyang Ren, Zhaoyang Liu, Yaliang Li +4
Recently, deep learning has made significant progress in the task of sequential recommendation. Existing neural sequential recommenders typically adopt a generative way trained wit…
Neural Ranking Models with Multiple Document Fields
Hamed Zamani, Bhaskar Mitra, Xia Song +2
Deep neural networks have recently shown promise in the ad-hoc retrieval task. However, such models have often been based on one field of the document, for example considering docu…
Luandri: a Clean Lua Interface to the Indri Search Engine
Bhaskar Mitra, Fernando Diaz, Nick Craswell
In recent years, the information retrieval (IR) community has witnessed the first successful applications of deep neural network models to short-text matching and ad-hoc retrieval.…
Learning to Match Using Local and Distributed Representations of Text for Web Search
Bhaskar Mitra, Fernando Diaz, Nick Craswell
Models such as latent semantic analysis and those based on neural embeddings learn distributed representations of text, and match the query against the document in the latent seman…