VideoRecSys + LargeRecSys 2024
With the exponential growth of video and other content across various domains including entertainment, e-commerce, education and social media, there is a growing need for personalized content recommendations that are relevant to users’ interests. However, building effective and scalable content recommender systems is challenging due to factors such as the vast volume of content, diversity of user preferences, inherent noise and bias in data, and the need for real-time recommendations.
Authors
- D.T.K. Tong (ORCID: https://orcid.org/0000-0003-3148-280X)
- Khushhall Chandra Mahajan (ORCID: https://orcid.org/0000-0003-3054-1142)
- Arnab Bhadury (ORCID: https://orcid.org/0009-0004-3600-0667)
- Liang Liu (ORCID: https://orcid.org/0009-0008-1248-1012)
- Amey Porobo Dharwadker (ORCID: https://orcid.org/0000-0002-0209-8406)
- Brad Schumitsch (ORCID: https://orcid.org/0000-0002-2908-6544)
- Saurabh Gupta (ORCID: https://orcid.org/0009-0002-3337-8408)
- Ko-Jen Hsiao
Institutions
- Google (United States) (US)
- Google (Canada) (CA)
- Netflix (United States) (US)
Publication Details
- Published
- 2024-10-08
- DOI
- https://doi.org/10.1145/3640457.3687116
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00