10 citations · 28 across the 10 of their papers we have counts for
10 papers · 1 filter
Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous Decoding
Hansi Zeng, Chen Luo, Hamed Zamani
This paper introduces PAG-a novel optimization and decoding approach that guides autoregressive generation of document identifiers in generative retrieval models through simultaneo…
Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond
Tianxin Wei, Bowen Jin, Ruirui Li +8
Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration. Ou…
Scalable and Effective Generative Information Retrieval
Hansi Zeng, Chen Luo, Bowen Jin +3
Recent research has shown that transformer networks can be used as differentiable search indexes by representing each document as a sequences of document ID tokens. These generativ…
Language Models As Semantic Indexers
Bowen Jin, Hansi Zeng, Guoyin Wang +10
Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs. Previous stud…
Soft Prompt Decoding for Multilingual Dense Retrieval
Zhiqi Huang, Hansi Zeng, Hamed Zamani +1
In this work, we explore a Multilingual Information Retrieval (MLIR) task, where the collection includes documents in multiple languages. We demonstrate that applying state-of-the-…
A Personalized Dense Retrieval Framework for Unified Information Access
Hansi Zeng, Surya Kallumadi, Zaid Alibadi +2
Developing a universal model that can efficiently and effectively respond to a wide range of information access requests -- from retrieval to recommendation to question answering -…
Curriculum Learning for Dense Retrieval Distillation
Hansi Zeng, Hamed Zamani, Vishwa Vinay
Recent work has shown that more effective dense retrieval models can be obtained by distilling ranking knowledge from an existing base re-ranking model. In this paper, we propose a…
Understanding the Effectiveness of Reviews in E-commerce Top-N Recommendation
Zhichao Xu, Hansi Zeng, Qingyao Ai
Modern E-commerce websites contain heterogeneous sources of information, such as numerical ratings, textual reviews and images. These information can be utilized to assist recommen…
A Zero Attentive Relevance Matching Networkfor Review Modeling in Recommendation System
Hansi Zeng, Zhichao Xu, Qingyao Ai
User and item reviews are valuable for the construction of recommender systems. In general, existing review-based methods for recommendation can be broadly categorized into two gro…
A Hierarchical Self-attentive Convolution Network for Review Modeling in Recommendation Systems
Hansi Zeng, Qingyao Ai
Using reviews to learn user and item representations is important for recommender system. Current review based methods can be divided into two categories: (1) the Convolution Neura…