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Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendation
The Web Conference, 2023Recommendation systems help users find matched items based on their previous behaviors. Personalized recommendation becomes challenging in the absence of historical user-item interactions, a practical problem for startups known as the system cold-start ...
Xuansheng Wu +5 more
semanticscholar +1 more source
Environmental Science and Technology, 2023
It remains a major challenge to abate efficiently the harmful nitrogen oxides (NOx) in low-temperature diesel exhausts emitted during the cold-start period of engine operation.
Ying Li +9 more
semanticscholar +1 more source
It remains a major challenge to abate efficiently the harmful nitrogen oxides (NOx) in low-temperature diesel exhausts emitted during the cold-start period of engine operation.
Ying Li +9 more
semanticscholar +1 more source
2014 IEEE 17th International Conference on Computational Science and Engineering, 2014
Cold-Start is one of the most difficult problems faced by web companies today in the domain of recommendation system. Cold-Start problem refers to predicting the behavior of a new user/item having no history. Common algorithms used to predict users behavior fail at addressing the cold-start problem because the algorithms are based on the user history ...
Lebi Jean-Marc Dali, Qin Zhiguang
openaire +1 more source
Cold-Start is one of the most difficult problems faced by web companies today in the domain of recommendation system. Cold-Start problem refers to predicting the behavior of a new user/item having no history. Common algorithms used to predict users behavior fail at addressing the cold-start problem because the algorithms are based on the user history ...
Lebi Jean-Marc Dali, Qin Zhiguang
openaire +1 more source
Item cold-start recommendations
Proceedings of the 8th ACM Conference on Recommender systems, 2014Recommender systems suggest to users items that they might like (e.g., news articles, songs, movies) and, in doing so, they help users deal with information overload and enjoy a personalized experience. One of the main problems of these systems is the item cold-start, i.e., when a new item is introduced in the system and no past information is ...
Martin Saveski, Amin Mantrach
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Cold-Start Representation Learning
Proceedings of the 27th ACM International Conference on Multimedia, 2019Video relevance computation is one of the most important tasks for the personalized online streaming service. Given the relevance of videos and viewer feedbacks, the system can provide personalized recommendations, which helps viewers discover more contents of interest in most online services.
Xinran Zhang +3 more
openaire +1 more source
GoRec: A Generative Cold-start Recommendation Framework
ACM Multimedia, 2023Multimedia-based recommendation models learn user and item preference representation by fusing both the user-item collaborative signals and the multimedia content signals.
Haoyue Bai +6 more
semanticscholar +1 more source
Proceedings of the 13th International Conference on Mining Software Repositories, 2016
Software project artifacts such as source code, requirements, and change logs represent a gold-mine of actionable information. As a result, software analytic solutions have been developed to mine repositories and answer questions such as "who is the expert?,'' "which classes are fault prone?,'' or even "who are the domain experts for these fault-prone ...
Jin Guo +5 more
openaire +1 more source
Software project artifacts such as source code, requirements, and change logs represent a gold-mine of actionable information. As a result, software analytic solutions have been developed to mine repositories and answer questions such as "who is the expert?,'' "which classes are fault prone?,'' or even "who are the domain experts for these fault-prone ...
Jin Guo +5 more
openaire +1 more source
Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating
The Web Conference, 2023How can we recommend cold-start bundles to users? The cold-start problem in bundle recommendation is crucial because new bundles are continuously created on the Web for various marketing purposes.
Hyunsik Jeon +3 more
semanticscholar +1 more source
Contrastive Proxy Kernel Stein Path Alignment for Cross-Domain Cold-Start Recommendation
IEEE Transactions on Knowledge and Data Engineering, 2023Cross-Domain Recommendation has been popularly studied to utilize different domain knowledge to solve the cold-start problem in recommender systems. In this paper, we focus on the Cross-Domain Cold-Start Recommendation (CDCSR) problem.
Weiming Liu +5 more
semanticscholar +1 more source
Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning
arXiv.orgInspired by the remarkable reasoning capabilities of Deepseek-R1 in complex textual tasks, many works attempt to incentivize similar capabilities in Multimodal Large Language Models (MLLMs) by directly applying reinforcement learning (RL).
Shuang Chen +9 more
semanticscholar +1 more source