Zobrazeno 1 - 10
of 589
pro vyhledávání: '"P. Achan"'
Autor:
Ma, Luyi, Li, Xiaohan, Fan, Zezhong, Xu, Jianpeng, Cho, Jason, Kanumala, Praveen, Nag, Kaushiki, Kumar, Sushant, Achan, Kannan
Integrating diverse data modalities is crucial for enhancing the performance of personalized recommendation systems. Traditional models, which often rely on singular data sources, lack the depth needed to accurately capture the multifaceted nature of
Externí odkaz:
http://arxiv.org/abs/2410.12228
Decoding Style: Efficient Fine-Tuning of LLMs for Image-Guided Outfit Recommendation with Preference
Personalized outfit recommendation remains a complex challenge, demanding both fashion compatibility understanding and trend awareness. This paper presents a novel framework that harnesses the expressive power of large language models (LLMs) for this
Externí odkaz:
http://arxiv.org/abs/2409.12150
E-commerce platforms have a vast catalog of items to cater to their customers' shopping interests. Most of these platforms assist their customers in the shopping process by offering optimized recommendation carousels, designed to help customers quick
Externí odkaz:
http://arxiv.org/abs/2409.07627
Autor:
Fan, Zezhong, Li, Xiaohan, Fang, Chenhao, Biswas, Topojoy, Nag, Kaushiki, Xu, Jianpeng, Achan, Kannan
The rapid evolution of text-to-image diffusion models has opened the door of generative AI, enabling the translation of textual descriptions into visually compelling images with remarkable quality. However, a persistent challenge within this domain i
Externí odkaz:
http://arxiv.org/abs/2404.11589
Autor:
Fang, Chenhao, Li, Xiaohan, Fan, Zezhong, Xu, Jianpeng, Nag, Kaushiki, Korpeoglu, Evren, Kumar, Sushant, Achan, Kannan
Product attribute value extraction is a pivotal component in Natural Language Processing (NLP) and the contemporary e-commerce industry. The provision of precise product attribute values is fundamental in ensuring high-quality recommendations and enh
Externí odkaz:
http://arxiv.org/abs/2403.00863
Autor:
Vashishtha, Shanu, Prakash, Abhinav, Morishetti, Lalitesh, Nag, Kaushiki, Arora, Yokila, Kumar, Sushant, Achan, Kannan
Text-to-image models such as stable diffusion have opened a plethora of opportunities for generating art. Recent literature has surveyed the use of text-to-image models for enhancing the work of many creative artists. Many e-commerce platforms employ
Externí odkaz:
http://arxiv.org/abs/2403.05578
Many current recommender systems mainly focus on the product-to-product recommendations and user-to-product recommendations even during the time of events rather than modeling the typical recommendations for the target event (e.g., festivals, seasona
Externí odkaz:
http://arxiv.org/abs/2402.03277
Autor:
Forouzandehmehr, Najmeh, Cao, Yijie, Thakurdesai, Nikhil, Giahi, Ramin, Ma, Luyi, Farrokhsiar, Nima, Xu, Jianpeng, Korpeoglu, Evren, Achan, Kannan
The outfit generation problem involves recommending a complete outfit to a user based on their interests. Existing approaches focus on recommending items based on anchor items or specific query styles but do not consider customer interests in famous
Externí odkaz:
http://arxiv.org/abs/2402.05941
Autor:
Ma, Luyi, Thakurdesai, Nikhil, Chen, Jiao, Xu, Jianpeng, Korpeoglu, Evren, Kumar, Sushant, Achan, Kannan
Data processing is one of the fundamental steps in machine learning pipelines to ensure data quality. Majority of the applications consider the user-defined function (UDF) design pattern for data processing in databases. Although the UDF design patte
Externí odkaz:
http://arxiv.org/abs/2312.16351
Autor:
Ye, Zikun, Maragheh, Reza Yousefi, Morishetti, Lalitesh, Vashishtha, Shanu, Cho, Jason, Nag, Kaushiki, Kumar, Sushant, Achan, Kannan
This paper aims to investigate and achieve seller-side fairness within online marketplaces, where many sellers and their items are not sufficiently exposed to customers in an e-commerce platform. This phenomenon raises concerns regarding the potentia
Externí odkaz:
http://arxiv.org/abs/2312.03253