Search Results for author: Joemon Jose

Found 6 papers, 3 papers with code

CFIR: Fast and Effective Long-Text To Image Retrieval for Large Corpora

no code implementations23 Feb 2024 Zijun Long, Xuri Ge, Richard McCreadie, Joemon Jose

Text-to-image retrieval aims to find the relevant images based on a text query, which is important in various use-cases, such as digital libraries, e-commerce, and multimedia databases.

Computational Efficiency Image Retrieval +2

Label Denoising through Cross-Model Agreement

no code implementations27 Aug 2023 Yu Wang, Xin Xin, Zaiqiao Meng, Joemon Jose, Fuli Feng

We employ the proposed DeCA on both the binary label scenario and the multiple label scenario.

Denoising Image Classification

Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment

1 code implementation9 May 2023 Xin Xin, Xiangyuan Liu, Hanbing Wang, Pengjie Ren, Zhumin Chen, Jiahuan Lei, Xinlei Shi, Hengliang Luo, Joemon Jose, Maarten de Rijke, Zhaochun Ren

Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target behavior such as purchases.

Denoising Open-Ended Question Answering +2

Learning Robust Recommenders through Cross-Model Agreement

no code implementations20 May 2021 Yu Wang, Xin Xin, Zaiqiao Meng, Xiangnan He, Joemon Jose, Fuli Feng

A noisy negative example which is uninteracted because of unawareness of the user could also denote potential positive user preference.

Denoising Recommendation Systems

One Person, One Model, One World: Learning Continual User Representation without Forgetting

2 code implementations29 Sep 2020 Fajie Yuan, Guoxiao Zhang, Alexandros Karatzoglou, Joemon Jose, Beibei Kong, Yudong Li

In this paper, we delve on research to continually learn user representations task by task, whereby new tasks are learned while using partial parameters from old ones.

Recommendation Systems

Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation

2 code implementations29 Apr 2019 Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, Joemon Jose

In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system.

Collaborative Filtering Recommendation Systems +1

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