Zobrazeno 1 - 10
of 32 576
pro vyhledávání: '"computer science - information retrieval"'
Autor:
Zhang, Weizhi, Bei, Yuanchen, Yang, Liangwei, Zou, Henry Peng, Zhou, Peilin, Liu, Aiwei, Li, Yinghui, Chen, Hao, Wang, Jianling, Wang, Yu, Huang, Feiran, Zhou, Sheng, Bu, Jiajun, Lin, Allen, Caverlee, James, Karray, Fakhri, King, Irwin, Yu, Philip S.
Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recommendations. Due to the diversification of internet platforms and the ex
Externí odkaz:
http://arxiv.org/abs/2501.01945
Autor:
Hachmeier, Simon, Jäschke, Robert
Recent advances in cover song identification have shown great success. However, models are usually tested on a fixed set of datasets which are relying on the online cover song database SecondHandSongs. It is unclear how well models perform on cover s
Externí odkaz:
http://arxiv.org/abs/2501.01333
Publikováno v:
Proceedings of the SPIE Medical Imaging, 16--20 February, 2025, San Diego, California, US
When conducting large-scale studies that collect brain MR images from multiple facilities, the impact of differences in imaging equipment and protocols at each site cannot be ignored, and this domain gap has become a significant issue in recent years
Externí odkaz:
http://arxiv.org/abs/2501.01326
Autor:
Mushtaque, Uzma
Transformer based models are increasingly being used in various domains including recommender systems (RS). Pretrained transformer models such as BERT have shown good performance at language modelling. With the greater ability to model sequential tas
Externí odkaz:
http://arxiv.org/abs/2501.01242
Autor:
Sar, Soumyadeep, Roy, Dwaipayan
This study investigates the several nuanced rationales for countering the rise of political bias. We evaluate the performance of the Llama-3 (70B) language model on the Media Bias Identification Benchmark (MBIB), based on a novel prompting technique
Externí odkaz:
http://arxiv.org/abs/2501.00782
Autor:
Hu, Xiyang
The increasing integration of Large Language Model (LLM) based search engines has transformed the landscape of information retrieval. However, these systems are vulnerable to adversarial attacks, especially ranking manipulation attacks, where attacke
Externí odkaz:
http://arxiv.org/abs/2501.00745
Autor:
Dudy, Shiran
In light of Phillips' contention regarding the impracticality of Search Neutrality, asserting that non-epistemic factors presently dictate result prioritization, our objective in this study is to confront this constraint by questioning prevailing des
Externí odkaz:
http://arxiv.org/abs/2501.00987
Autor:
Man, Hieu, Ngo, Nghia Trung, Lai, Viet Dac, Rossi, Ryan A., Dernoncourt, Franck, Nguyen, Thien Huu
Recent advancements in large language models (LLMs) based embedding models have established new state-of-the-art benchmarks for text embedding tasks, particularly in dense vector-based retrieval. However, these models predominantly focus on English,
Externí odkaz:
http://arxiv.org/abs/2501.00874
The embedded topic model (ETM) is a widely used approach that assumes the sampled document-topic distribution conforms to the logistic normal distribution for easier optimization. However, this assumption oversimplifies the real document-topic distri
Externí odkaz:
http://arxiv.org/abs/2501.00862
The ability of perceiving fine-grained spatial and temporal information is crucial for video-language retrieval. However, the existing video retrieval benchmarks, such as MSRVTT and MSVD, fail to efficiently evaluate the fine-grained retrieval abilit
Externí odkaz:
http://arxiv.org/abs/2501.00513