Hongxia Yang
10 papers in the PaperMetrix corpus
Papers by this author
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Towards Knowledge-Based Personalized Product Description Generation in E-commerce
2019
Quality product descriptions are critical for providing competitive customer experience in an E-commerce platform. An accurate and attractive description not only helps customers make an informed decision but also improves the likelihood of purchase. However, …
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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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Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
2024 · arXiv (Cornell University)
Strong Artificial Intelligence (Strong AI) or Artificial General Intelligence (AGI) with abstract reasoning ability is the goal of next-generation AI. Recent advancements in Large Language Models (LLMs), along with the emerging field of Multimodal Large …
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$\mathbf{(N,K)}$-Puzzle: A Cost-Efficient Testbed for Benchmarking Reinforcement Learning Algorithms in Generative Language Model
2024 · arXiv (Cornell University)
Recent advances in reinforcement learning (RL) algorithms aim to enhance the performance of language models at scale. Yet, there is a noticeable absence of a cost-effective and standardized testbed tailored to evaluating and comparing these …
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Representation Learning for Attributed Multiplex Heterogeneous Network
2019
Network embedding (or graph embedding) has been widely used in many real-world applications. However, existing methods mainly focus on networks with single-typed nodes/edges and cannot scale well to handle large networks. Many real-world networks consist …
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Towards Knowledge-Based Recommender Dialog System
2019
Qibin Chen, Junyang Lin, Yichang Zhang, Ming Ding, Yukuo Cen, Hongxia Yang, Jie Tang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language …
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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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GCC
2020
Graph representation learning has emerged as a powerful technique for addressing real-world problems. Various downstream graph learning tasks have benefited from its recent developments, such as node classification, similarity search, and graph classification. However, prior …
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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 …