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pro vyhledávání: '"Kang, SeongKu"'
Language Models (LMs) are increasingly employed in recommendation systems due to their advanced language understanding and generation capabilities. Recent recommender systems based on generative retrieval have leveraged the inferential abilities of L
Externí odkaz:
http://arxiv.org/abs/2408.08686
Autor:
Kim, Jieyong, Kim, Hyunseo, Cho, Hyunjin, Kang, SeongKu, Chang, Buru, Yeo, Jinyoung, Lee, Dongha
Recent advancements in Large Language Models (LLMs) have demonstrated exceptional performance across a wide range of tasks, generating significant interest in their application to recommendation systems. However, existing methods have not fully capit
Externí odkaz:
http://arxiv.org/abs/2408.06276
Cross-lingual entity alignment (EA) enables the integration of multiple knowledge graphs (KGs) across different languages, providing users with seamless access to diverse and comprehensive knowledge. Existing methods, mostly supervised, face challeng
Externí odkaz:
http://arxiv.org/abs/2407.15588
Autor:
Kang, SeongKu
Despite its breakthrough in classification problems, Knowledge distillation (KD) to recommendation models and ranking problems has not been studied well in the previous literature. This dissertation is devoted to developing knowledge distillation met
Externí odkaz:
http://arxiv.org/abs/2407.13952
Cross-domain recommendation (CDR) extends conventional recommender systems by leveraging user-item interactions from dense domains to mitigate data sparsity and the cold start problem. While CDR offers substantial potential for enhancing recommendati
Externí odkaz:
http://arxiv.org/abs/2407.12374
Label noise, commonly found in real-world datasets, has a detrimental impact on a model's generalization. To effectively detect incorrectly labeled instances, previous works have mostly relied on distinguishable training signals, such as training los
Externí odkaz:
http://arxiv.org/abs/2405.19902
Knowledge distillation (KD) has emerged as a promising technique for addressing the computational challenges associated with deploying large-scale recommender systems. KD transfers the knowledge of a massive teacher system to a compact student model,
Externí odkaz:
http://arxiv.org/abs/2405.19046
Recently, web platforms have been operating various service domains simultaneously. Targeting a platform that operates multiple service domains, we introduce a new task, Multi-Domain Recommendation to Attract Users (MDRAU), which recommends items fro
Externí odkaz:
http://arxiv.org/abs/2403.17374
Matrix completion is an important area of research in recommender systems. Recent methods view a rating matrix as a user-item bi-partite graph with labeled edges denoting observed ratings and predict the edges between the user and item nodes by using
Externí odkaz:
http://arxiv.org/abs/2403.04504
Autor:
Kim, Minjin, Kim, Minju, Kim, Hana, Kwak, Beong-woo, Chun, Soyeon, Kim, Hyunseo, Kang, SeongKu, Yu, Youngjae, Yeo, Jinyoung, Lee, Dongha
Conversational recommender system is an emerging area that has garnered an increasing interest in the community, especially with the advancements in large language models (LLMs) that enable diverse reasoning over conversational input. Despite the pro
Externí odkaz:
http://arxiv.org/abs/2403.04460