Chang Zhou
10 papers in the PaperMetrix corpus
Papers by this author
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Disentangled Self-Supervision in Sequential Recommenders
2020
To learn a sequential recommender, the existing methods typically adopt the sequence-to-item (seq2item) training strategy, which supervises a sequence model with a user's next behavior as the label and the user's past behaviors as the …
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M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems
2022 · arXiv (Cornell University)
Industrial recommender systems have been growing increasingly complex, may involve \emph{diverse domains} such as e-commerce products and user-generated contents, and can comprise \emph{a myriad of tasks} such as retrieval, ranking, explanation generation, and even AI-assisted …
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Class imbalance: A crucial factor affecting the performance of tea plantations mapping by machine learning
2024 · International Journal of Applied Earth Observation and Geoinformation
Due to disparities in area among various land cover types, class imbalance has always existed in crop mapping research, posing uncertainties in extracting minority classes which occupy a smaller area. In this paper, taking tea …
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MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs
2024
Graph Neural Networks (GNNs) have emerged as a mainstream technique for graph representation learning. However, their efficacy within an end-to-end supervised framework is significantly tied to the availability of task-specific labels. To mitigate labeling costs …
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How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
2024
Guanting Dong, Hongyi Yuan, Keming Lu, Chengpeng Li, Mingfeng Xue, Dayiheng Liu, Wei Wang, Zheng Yuan, Chang Zhou, Jingren Zhou. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long …
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ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation
2017 · arXiv (Cornell University)
A user can be represented as what he/she does along the history. A common way to deal with the user modeling problem is to manually extract all kinds of aggregated features over the heterogeneous behaviors, …
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Deep Interest Evolution Network for Click-Through Rate Prediction
2019 · Proceedings of the AAAI Conference on Artificial Intelligence
Click-through rate (CTR) prediction, whose goal is to estimate the probability of a user clicking on the item, has become one of the core tasks in the advertising system. For CTR prediction model, it is …
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ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation
2018 · Proceedings of the AAAI Conference on Artificial Intelligence
A user can be represented as what he/she does along the history. A common way to deal with the user modeling problem is to manually extract all kinds of aggregated features over the heterogeneous behaviors, …
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Learning Disentangled Representations for Recommendation
2019 · arXiv (Cornell University)
User behavior data in recommender systems are driven by the complex interactions of many latent factors behind the users' decision making processes. The factors are highly entangled, and may range from high-level ones that govern …
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Controllable Multi-Interest Framework for Recommendation
2020
Recently, neural networks have been widely used in e-commerce recommender systems, owing to the rapid development of deep learning. We formalize the recommender system as a sequential recommendation problem, intending to predict the next items …