Search Results for author: Jiahao Wu

Found 13 papers, 4 papers with code

Discrete Semantic Tokenization for Deep CTR Prediction

2 code implementations13 Mar 2024 Qijiong Liu, Hengchang Hu, Jiahao Wu, Jieming Zhu, Min-Yen Kan, Xiao-Ming Wu

Incorporating item content information into click-through rate (CTR) prediction models remains a challenge, especially with the time and space constraints of industrial scenarios.

Click-Through Rate Prediction News Recommendation

Dataset Condensation for Recommendation

no code implementations2 Oct 2023 Jiahao Wu, Wenqi Fan, Shengcai Liu, Qijiong Liu, Rui He, Qing Li, Ke Tang

However, applying existing approaches to condense recommendation datasets is impractical due to following challenges: (i) sampling-based methods are inadequate in addressing the long-tailed distribution problem; (ii) synthesizing-based methods are not applicable due to discreteness of interactions and large size of recommendation datasets; (iii) neither of them fail to address the specific issue in recommendation of false negative items, where items with potential user interest are incorrectly sampled as negatives owing to insufficient exposure.

Dataset Condensation

Enhancing Graph Collaborative Filtering via Uniformly Co-Clustered Intent Modeling

no code implementations22 Sep 2023 Jiahao Wu, Wenqi Fan, Shengcai Liu, Qijiong Liu, Qing Li, Ke Tang

To model the compatibility between user intents and item properties, we design the user-item co-clustering module, maximizing the mutual information of co-clusters of users and items.

Collaborative Filtering

Multi-Domain Learning From Insufficient Annotations

no code implementations4 May 2023 Rui He, Shengcai Liu, Jiahao Wu, Shan He, Ke Tang

Multi-domain learning (MDL) refers to simultaneously constructing a model or a set of models on datasets collected from different domains.

Active Learning Contrastive Learning

FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation

1 code implementation2 Apr 2023 Qijiong Liu, Jieming Zhu, Jiahao Wu, Tiandeng Wu, Zhenhua Dong, Xiao-Ming Wu

Item list continuation is proposed to model the overall trend of a list and predict subsequent items.

Simultaneous upper and lower bounds of American option prices with hedging via neural networks

1 code implementation24 Feb 2023 Ivan Guo, Nicolas Langrené, Jiahao Wu

In this paper, we introduce two methods to solve the American-style option pricing problem and its dual form at the same time using neural networks.

Disentangled Contrastive Learning for Social Recommendation

1 code implementation18 Aug 2022 Jiahao Wu, Wenqi Fan, Jingfan Chen, Shengcai Liu, Qing Li, Ke Tang

In this work, to address such limitation, we propose a novel Disentangled contrastive learning framework for social Recommendations DcRec.

Contrastive Learning Representation Learning +1

Two-Step Image Dehazing with Intra-domain and Inter-domain Adaptation

no code implementations6 Feb 2021 Xin Yi, Bo Ma, Yulin Zhang, Longyao Liu, Jiahao Wu

To alleviate the intra-domain gap of the synthetic domain, we propose an intra-domain adaptation to align distributions of other subsets to the optimal subset by adversarial learning.

Domain Adaptation Image Dehazing +1

Reinforcement Learning-based Product Delivery Frequency Control

no code implementations20 Dec 2020 Yang Liu, Zhengxing Chen, Kittipat Virochsiri, Juan Wang, Jiahao Wu, Feng Liang

We demonstrate statistically significant improvement in daily metrics and resource efficiency by our method in several notification applications at a scale of billions of users.

Recommendation Systems reinforcement-learning +1

paper2repo: GitHub Repository Recommendation for Academic Papers

no code implementations13 Apr 2020 Huajie Shao, Dachun Sun, Jiahao Wu, Zecheng Zhang, Aston Zhang, Shuochao Yao, Shengzhong Liu, Tianshi Wang, Chao Zhang, Tarek Abdelzaher

Motivated by this trend, we describe a novel item-item cross-platform recommender system, $\textit{paper2repo}$, that recommends relevant repositories on GitHub that match a given paper in an academic search system such as Microsoft Academic.

Recommendation Systems

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