Zobrazeno 1 - 10
of 690
pro vyhledávání: '"A., Sathiamoorthy"'
Autor:
Deldjoo, Yashar, He, Zhankui, McAuley, Julian, Korikov, Anton, Sanner, Scott, Ramisa, Arnau, Vidal, Rene, Sathiamoorthy, Maheswaran, Kasrizadeh, Atoosa, Milano, Silvia, Ricci, Francesco
Generative models are a class of AI models capable of creating new instances of data by learning and sampling from their statistical distributions. In recent years, these models have gained prominence in machine learning due to the development of app
Externí odkaz:
http://arxiv.org/abs/2409.15173
Autor:
Ramisa, Arnau, Vidal, Rene, Deldjoo, Yashar, He, Zhankui, McAuley, Julian, Korikov, Anton, Sanner, Scott, Sathiamoorthy, Mahesh, Kasrizadeh, Atoosa, Milano, Silvia, Ricci, Francesco
Many recommendation systems limit user inputs to text strings or behavior signals such as clicks and purchases, and system outputs to a list of products sorted by relevance. With the advent of generative AI, users have come to expect richer levels of
Externí odkaz:
http://arxiv.org/abs/2409.10993
Autor:
Korikov, Anton, Sanner, Scott, Deldjoo, Yashar, He, Zhankui, McAuley, Julian, Ramisa, Arnau, Vidal, Rene, Sathiamoorthy, Mahesh, Kasrizadeh, Atoosa, Milano, Silvia, Ricci, Francesco
While previous chapters focused on recommendation systems (RSs) based on standardized, non-verbal user feedback such as purchases, views, and clicks -- the advent of LLMs has unlocked the use of natural language (NL) interactions for recommendation.
Externí odkaz:
http://arxiv.org/abs/2408.10946
Autor:
Deldjoo, Yashar, He, Zhankui, McAuley, Julian, Korikov, Anton, Sanner, Scott, Ramisa, Arnau, Vidal, René, Sathiamoorthy, Maheswaran, Kasirzadeh, Atoosa, Milano, Silvia
Traditional recommender systems (RS) typically use user-item rating histories as their main data source. However, deep generative models now have the capability to model and sample from complex data distributions, including user-item interactions, te
Externí odkaz:
http://arxiv.org/abs/2404.00579
Autor:
Cao, Yuwei, Mehta, Nikhil, Yi, Xinyang, Keshavan, Raghunandan, Heldt, Lukasz, Hong, Lichan, Chi, Ed H., Sathiamoorthy, Maheswaran
Large language models (LLMs) have recently been used as backbones for recommender systems. However, their performance often lags behind conventional methods in standard tasks like retrieval. We attribute this to a mismatch between LLMs' knowledge and
Externí odkaz:
http://arxiv.org/abs/2404.00245
Autor:
Singh, Anima, Vu, Trung, Mehta, Nikhil, Keshavan, Raghunandan, Sathiamoorthy, Maheswaran, Zheng, Yilin, Hong, Lichan, Heldt, Lukasz, Wei, Li, Tandon, Devansh, Chi, Ed H., Yi, Xinyang
Randomly-hashed item ids are used ubiquitously in recommendation models. However, the learned representations from random hashing prevents generalization across similar items, causing problems of learning unseen and long-tail items, especially when i
Externí odkaz:
http://arxiv.org/abs/2306.08121
Autor:
Kang, Wang-Cheng, Ni, Jianmo, Mehta, Nikhil, Sathiamoorthy, Maheswaran, Hong, Lichan, Chi, Ed, Cheng, Derek Zhiyuan
Large Language Models (LLMs) have demonstrated exceptional capabilities in generalizing to new tasks in a zero-shot or few-shot manner. However, the extent to which LLMs can comprehend user preferences based on their previous behavior remains an emer
Externí odkaz:
http://arxiv.org/abs/2305.06474
Autor:
Rajput, Shashank, Mehta, Nikhil, Singh, Anima, Keshavan, Raghunandan H., Vu, Trung, Heldt, Lukasz, Hong, Lichan, Tay, Yi, Tran, Vinh Q., Samost, Jonah, Kula, Maciej, Chi, Ed H., Sathiamoorthy, Maheswaran
Modern recommender systems perform large-scale retrieval by first embedding queries and item candidates in the same unified space, followed by approximate nearest neighbor search to select top candidates given a query embedding. In this paper, we pro
Externí odkaz:
http://arxiv.org/abs/2305.05065
Autor:
Tang, Jiaxi, Drori, Yoel, Chang, Daryl, Sathiamoorthy, Maheswaran, Gilmer, Justin, Wei, Li, Yi, Xinyang, Hong, Lichan, Chi, Ed H.
Recommender systems play an important role in many content platforms. While most recommendation research is dedicated to designing better models to improve user experience, we found that research on stabilizing the training for such models is severel
Externí odkaz:
http://arxiv.org/abs/2302.09178
Publikováno v:
Cogent Education, Vol 11, Iss 1 (2024)
AbstractWith the emerging trend of researching teacher identity from using qualitative approach to adopting quantitative or mixed-methods approach, it is essential and significant to develop instruments for measuring this construct. This paper report
Externí odkaz:
https://doaj.org/article/3a5f25d80f3e4864901ffb7202155948
