Zobrazeno 1 - 10
of 7 530
pro vyhledávání: '"Recommendation models"'
Autor:
Guo, Wei, Wang, Hao, Zhang, Luankang, Chin, Jin Yao, Liu, Zhongzhou, Cheng, Kai, Pan, Qiushi, Lee, Yi Quan, Xue, Wanqi, Shen, Tingjia, Song, Kenan, Wang, Kefan, Xie, Wenjia, Ye, Yuyang, Guo, Huifeng, Liu, Yong, Lian, Defu, Tang, Ruiming, Chen, Enhong
Recommendation systems are essential for filtering data and retrieving relevant information across various applications. Recent advancements have seen these systems incorporate increasingly large embedding tables, scaling up to tens of terabytes for
Externí odkaz:
http://arxiv.org/abs/2412.00714
Autor:
Maharjan, Puja
Citation recommendation systems have attracted much academic interest, resulting in many studies and implementations. These systems help authors automatically generate proper citations by suggesting relevant references based on the text they have wri
Externí odkaz:
http://arxiv.org/abs/2412.07713
We study user history modeling via Transformer encoders in deep learning recommendation models (DLRM). Such architectures can significantly improve recommendation quality, but usually incur high latency cost necessitating infrastructure upgrades or v
Externí odkaz:
http://arxiv.org/abs/2412.06924
Modeling user preferences has been mainly addressed by looking at users' interaction history with the different elements available in the system. Tailoring content to individual preferences based on historical data is the main goal of sequential reco
Externí odkaz:
http://arxiv.org/abs/2412.07585
Autor:
Ahmad, Ghazi Shazan, Agarwal, Shubham, Mitra, Subrata, Rossi, Ryan, Doshi, Manav, Porwal, Vibhor, Paila, Syam Manoj Kumar
Automated visualization recommendations (vis-rec) help users to derive crucial insights from new datasets. Typically, such automated vis-rec models first calculate a large number of statistics from the datasets and then use machine-learning models to
Externí odkaz:
http://arxiv.org/abs/2411.18657
Large-scale recommendation models are currently the dominant workload for many large Internet companies. These recommenders are characterized by massive embedding tables that are sparsely accessed by the index for user and item features. The size of
Externí odkaz:
http://arxiv.org/abs/2410.20046
Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation Models
Large-scale industrial recommendation models predict the most relevant items from catalogs containing millions or billions of options. To train these models efficiently, a small set of irrelevant items (negative samples) is selected from the vast cat
Externí odkaz:
http://arxiv.org/abs/2410.17276
Autor:
Feng, Ningya, Pan, Junwei, Wu, Jialong, Chen, Baixu, Wang, Ximei, Li, Qian, Hu, Xian, Jiang, Jie, Long, Mingsheng
Lifelong user behavior sequences, comprising up to tens of thousands of history behaviors, are crucial for capturing user interests and predicting user responses in modern recommendation systems. A two-stage paradigm is typically adopted to handle th
Externí odkaz:
http://arxiv.org/abs/2410.02604
Autor:
Jain, Kshitij, Xie, Jingru, Regan, Kevin, Chen, Cheng, Han, Jie, Li, Steve, Li, Zhuoshu, Phillips, Todd, Sussman, Myles, Troup, Matt, Yu, Angel, Zhuo, Jia
Large recommendation models (LRMs) are fundamental to the multi-billion dollar online advertising industry, processing massive datasets of hundreds of billions of examples before transitioning to continuous online training to adapt to rapidly changin
Externí odkaz:
http://arxiv.org/abs/2410.18111