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of 157
pro vyhledávání: '"Zhao, Hongke"'
The explainability of recommendation systems is crucial for enhancing user trust and satisfaction. Leveraging large language models (LLMs) offers new opportunities for comprehensive recommendation logic generation. However, in existing related studie
Externí odkaz:
http://arxiv.org/abs/2407.02833
Data bias, e.g., popularity impairs the dynamics of two-sided markets within recommender systems. This overshadows the less visible but potentially intriguing long-tail items that could capture user interest. Despite the abundance of research surroun
Externí odkaz:
http://arxiv.org/abs/2406.17475
Cross-domain recommendation offers a potential avenue for alleviating data sparsity and cold-start problems. Embedding and mapping, as a classic cross-domain research genre, aims to identify a common mapping function to perform representation transfo
Externí odkaz:
http://arxiv.org/abs/2406.16494
Significant efforts have been directed toward integrating powerful Large Language Models (LLMs) with diverse modalities, particularly focusing on the fusion of vision, language, and audio data. However, the graph-structured data, inherently rich in s
Externí odkaz:
http://arxiv.org/abs/2406.15504
Recently, large language models (LLMs) have been widely researched in the field of graph machine learning due to their outstanding abilities in language comprehension and learning. However, the significant gap between natural language tasks and topol
Externí odkaz:
http://arxiv.org/abs/2406.13250
Large Language Models (LLMs) are increasingly prominent in the recommendation systems domain. Existing studies usually utilize in-context learning or supervised fine-tuning on task-specific data to align LLMs into recommendations. However, the substa
Externí odkaz:
http://arxiv.org/abs/2406.13235
Unsupervised domain adaptation aims to transfer rich knowledge from the annotated source domain to the unlabeled target domain with the same label space. One prevalent solution is the bi-discriminator domain adversarial network, which strives to iden
Externí odkaz:
http://arxiv.org/abs/2310.13959
Recently, Zero-Shot Node Classification (ZNC) has been an emerging and crucial task in graph data analysis. This task aims to predict nodes from unseen classes which are unobserved in the training process. Existing work mainly utilizes Graph Neural N
Externí odkaz:
http://arxiv.org/abs/2308.08563
Autor:
Wu, Likang, Li, Zhi, Zhao, Hongke, Wang, Zhefeng, Liu, Qi, Huai, Baoxing, Yuan, Nicholas Jing, Chen, Enhong
Zero-Shot Learning (ZSL), which aims at automatically recognizing unseen objects, is a promising learning paradigm to understand new real-world knowledge for machines continuously. Recently, the Knowledge Graph (KG) has been proven as an effective sc
Externí odkaz:
http://arxiv.org/abs/2306.08487
Cross-domain recommendation aims to leverage knowledge from multiple domains to alleviate the data sparsity and cold-start problems in traditional recommender systems. One popular paradigm is to employ overlapping user representations to establish do
Externí odkaz:
http://arxiv.org/abs/2301.11467