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of 7
pro vyhledávání: '"Sodhi, Sukhdeep"'
Autor:
Sayana, Krishna, Vasudeva, Raghavendra, Vasilevski, Yuri, Su, Kun, Hebert, Liam, Pham, Hubert, Jash, Ambarish, Sodhi, Sukhdeep
The recent advances in Large Language Model's generation and reasoning capabilities present an opportunity to develop truly conversational recommendation systems. However, effectively integrating recommender system knowledge into LLMs for natural lan
Externí odkaz:
http://arxiv.org/abs/2410.16780
Autor:
Hebert, Liam, Kyriakidi, Marialena, Pham, Hubert, Sayana, Krishna, Pine, James, Sodhi, Sukhdeep, Jash, Ambarish
Hybrid recommender systems, combining item IDs and textual descriptions, offer potential for improved accuracy. However, previous work has largely focused on smaller datasets and model architectures. This paper introduces Flare (Fusing Language model
Externí odkaz:
http://arxiv.org/abs/2409.11699
Autor:
Hebert, Liam, Sayana, Krishna, Jash, Ambarish, Karatzoglou, Alexandros, Sodhi, Sukhdeep, Doddapaneni, Sumanth, Cai, Yanli, Kuzmin, Dima
Understanding the nuances of a user's extensive interaction history is key to building accurate and personalized natural language systems that can adapt to evolving user preferences. To address this, we introduce PERSOMA, Personalized Soft Prompt Ada
Externí odkaz:
http://arxiv.org/abs/2408.00960
Modeling long histories plays a pivotal role in enhancing recommendation systems, allowing to capture user's evolving preferences, resulting in more precise and personalized recommendations. In this study we tackle the challenges of modeling long use
Externí odkaz:
http://arxiv.org/abs/2401.04858
Autor:
Gupta, Abhirut, Sai, Ananya B., Sproat, Richard, Vasilevski, Yuri, Ren, James S., Jash, Ambarish, Sodhi, Sukhdeep S., Raghuveer, Aravindan
A large number of people are forced to use the Web in a language they have low literacy in due to technology asymmetries. Written text in the second language (L2) from such users often contains a large number of errors that are influenced by their na
Externí odkaz:
http://arxiv.org/abs/2307.03322
Autor:
Sodhi, Sukhdeep S., Chio, Ellie Ka-In, Jash, Ambarish, Ontañón, Santiago, Apte, Ajit, Kumar, Ankit, Jeje, Ayooluwakunmi, Kuzmin, Dima, Fung, Harry, Cheng, Heng-Tze, Effrat, Jon, Bali, Tarush, Jindal, Nitin, Cao, Pei, Singh, Sarvjeet, Zhou, Senqiang, Khan, Tameen, Wankhede, Amol, Alzantot, Moustafa, Wu, Allen, Chandra, Tushar
As more and more online search queries come from voice, automatic speech recognition becomes a key component to deliver relevant search results. Errors introduced by automatic speech recognition (ASR) lead to irrelevant search results returned to the
Externí odkaz:
http://arxiv.org/abs/2105.09930
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