Titlebook: Advances in Information Retrieval; 46th European Confer Nazli Goharian,Nicola Tonellotto,Iadh Ounis Conference proceedings 2024 The Editor(

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書目名稱Advances in Information Retrieval被引頻次




書目名稱Advances in Information Retrieval被引頻次學科排名




書目名稱Advances in Information Retrieval年度引用




書目名稱Advances in Information Retrieval年度引用學科排名




書目名稱Advances in Information Retrieval讀者反饋




書目名稱Advances in Information Retrieval讀者反饋學科排名

NEXUS

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

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

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

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

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

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

向宇宙

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

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