Multiple domain user personalization

By Yucheng Low, Deepak K. Agarwal, Alex Smola. 2011

36 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Raymond J. Mooney
2. Joydeep Ghosh
3. Ayan Acharya
4. Dean Phillips Foster
5. Paolo Cremonesi
6. Taiji Suzuki
7. Alex Smola
8. Peter Christen

In Information Systems

1. Raymond J. Mooney
2. Joydeep Ghosh
3. Ayan Acharya
4. Ignacio Fernández-Tobías
5. Hayato Kobayashi
6. Dean Phillips Foster
7. Shlomo Berkovsky
8. Paolo Cremonesi

Citations in the corpus

• Deep Factors for Forecasting (2019, background): “In (Ahmed et al., 2012; Low et al., 2011), other combined local and global models are detailed.”
• Cross-domain Recommendation via Deep Domain Adaptation (2018, background): “Approaches to this problem can be broadly classified into two types: collaborative filtering and content-based filtering.”
• Active Multitask Learning Using Both Latent and Supervised Shared Topics (2014, methodology): “These include different learning methods, such as empirical risk minimization using group-sparse regularizers [20, 17], hierarchical Bayesian models [43, 23] and hidden conditional random fields [29].”
• Cross-Domain Recommender Systems (2015, background): “…task Linked-domain task Cross-domain task Cold start Shapira et al. 2013 [58] New user Winoto et al. 2008 [66] Cremonesi et al. 2011 [16] Low et al. 2011 [45] Hu et al. 2013 [31] Sahebi et al. 2013 [55] Berkovsky et al. 2007 [6] [7] Berkovsky et al. 2008 [8] Nakatsuji et al. 2010 [47]…”