武士精神
发表于 2025-3-21 19:52:32
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NEXUS
发表于 2025-3-21 21:25:03
Rashmi Anoop Patil,Seeram Ramakrishnaing discarded in recommendation. These two challenges limit the effective representation of users and items by existing methods. Inspired by self-supervised learning to mine supervision signals from data, in this paper, we focus on exploring contrastive learning based on knowledge graph enhancement,
harpsichord
发表于 2025-3-22 00:55:09
https://doi.org/10.1007/978-981-19-9700-6l focus on humor. To bolster Mu2STS, we have developed the SHMH (WARNING: This paper contains meme samples that are offensive in nature.) (.) dataset, designed for detecting sarcasm and humor in memes written in the Hindi language, which is the first of its kind to the best of our knowledge. Our emp
Peculate
发表于 2025-3-22 07:36:47
Circularity Assessment: Macro to Nanos representative for the entire web according to a baseline retrieval system on the ClueWeb22. Focussing on the product review genre, we find that only a small portion of product reviews on the web uses affiliate marketing, but the majority of all search results do. Of all affiliate networks, Amazon
puzzle
发表于 2025-3-22 10:00:22
https://doi.org/10.1007/978-3-031-49479-6xtension of Memory Networks, a neural network architecture that harnesses external memory to encapsulate information present in lengthy sequential data. The use of memory networks in recommendation use-cases remains limited in practice owing to their high memory cost, large compute requirements and
jumble
发表于 2025-3-22 14:18:55
Takuya Nakashima,Tsuyoshi Kawai waste compute resources by scoring documents that are not related to the query. In this work, we propose an alternative formulation of the document similarity graph. Rather than using document similarities, we propose a weighted bipartite graph that consists of both document nodes and query nodes.
ODIUM
发表于 2025-3-22 17:37:51
Operation and Maintenance Issues,ficient unlearning method that can remove a client’s contribution without compromising the overall ranker effectiveness and without needing to retrain the global ranker from scratch. A key challenge is how to measure whether the model has unlearned the contributions from the client . that has reques
Palpitation
发表于 2025-3-22 23:42:51
http://reply.papertrans.cn/15/1484/148375/148375_8.png
向宇宙
发表于 2025-3-23 04:55:05
Yong Jin,Jing-Xu Zhu,Zhi-Qing Yuearning passage ranking querysets demonstrate significant improvements in shallow and full-scale models in low-latency scenarios. For example, when the latency limit is 25 ms per query, MonoBERT-Large (a cross-encoder based on a full-scale BERT model) is only able to achieve NDCG@10 of 0.431 on TREC
BROOK
发表于 2025-3-23 07:30:33
Safia El Messaoudi,Alain R. Thierryated by the potential efficacy of patients’ personal context and visual gestures, we propose a transformer-based multi-task, multi-modal intent-recognition, and medical concern summary generation (.) system. Furthermore, we propose a multitasking framework for intent recognition and medical concern