Zobrazeno 1 - 10
of 69 086
pro vyhledávání: '"Cold start"'
Autor:
Lv, Xiaomin1 (AUTHOR) lvxiaomin@zjsru.edu.cn, Fang, Kai2 (AUTHOR) kaifang@zafu.edu.cn, Liu, Tongcun2 (AUTHOR) lvxiaomin@zjsru.edu.cn
Publikováno v:
Sensors (14248220). Sep2024, Vol. 24 Issue 17, p5510. 13p.
Autor:
Zhenwei Wang1 zhenweiwang2022@163.com, Binbin Sun2 sunbin_sdut@126.com, Di Huang1 huangdi934675559@163.com, Tianqi Gu1 tianqi.gu@outlook.com, Zhenhao Liu1 15163095630@163.com
Publikováno v:
IAENG International Journal of Applied Mathematics. Jul2024, Vol. 54 Issue 7, p1296-1302. 7p.
Computerized Adaptive Testing (CAT) aims to select the most appropriate questions based on the examinee's ability and is widely used in online education. However, existing CAT systems often lack initial understanding of the examinee's ability, requir
Externí odkaz:
http://arxiv.org/abs/2411.12182
Autor:
Luo, Yunze, Jiang, Yuezihan, Jiang, Yinjie, Chen, Gaode, Wang, Jingchi, Bian, Kaigui, Li, Peiyi, Zhang, Qi
With the rise of e-commerce and short videos, online recommender systems that can capture users' interests and update new items in real-time play an increasingly important role. In both online and offline recommendation, the cold-start problem due to
Externí odkaz:
http://arxiv.org/abs/2411.11225
The growth of recommender systems (RecSys) is driven by digitization and the need for personalized content in areas such as e-commerce and video streaming. The content in these systems often changes rapidly and therefore they constantly face the ongo
Externí odkaz:
http://arxiv.org/abs/2411.09065
Graphs play a central role in modeling complex relationships across various domains. Most graph learning methods rely heavily on neighborhood information, raising the question of how to handle cold-start nodes - nodes with no known connections within
Externí odkaz:
http://arxiv.org/abs/2411.01532
Autor:
Brody, Shaked, Lagziel, Shoval
Sequential recommendation systems often struggle to make predictions or take action when dealing with cold-start items that have limited amount of interactions. In this work, we propose SimRec - a new approach to mitigate the cold-start problem in se
Externí odkaz:
http://arxiv.org/abs/2410.22136
The cold start problem in recommender systems remains a critical challenge. Current solutions often train hybrid models on auxiliary data for both cold and warm users/items, potentially degrading the experience for the latter. This drawback limits th
Externí odkaz:
http://arxiv.org/abs/2410.14241
Knowledge Tracing (KT) is vital in educational data mining, enabling personalized learning by tracking learners' knowledge states and forecasting their academic outcomes. This study introduces the LOKT (Large Language Model Option-weighted Knowledge
Externí odkaz:
http://arxiv.org/abs/2410.12872
Recommendation models utilizing unique identities (IDs) to represent distinct users and items have dominated the recommender systems literature for over a decade. Since multi-modal content of items (e.g., texts and images) and knowledge graphs (KGs)
Externí odkaz:
http://arxiv.org/abs/2410.07654