A location-based news article recommendation with explicit localized semantic analysis

By Jeong Woo Son, A.-Yeong Kim, Seong-Bae Park. 2013

93 citations recorded by the source corpus.

Subfields: Geography, Planning and Development, 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 Geography, Planning and Development

1. Xing Xie
2. Dietmar Jannach
3. Michael Jugovac
4. Mozhgan Karimi
5. Yu Zheng

In Information Systems

1. Xing Xie
2. Fangzhao Wu
3. Chuhan Wu
4. Yongfeng Huang
5. Tao Qi
6. Hongwei Wang
7. Fuzheng Zhang
8. Minyi Guo

Citations in the corpus

• DKN: Deep Knowledge-Aware Network for News Recommendation (2018, methodology): “To alleviate the impact of information over-loading, it is critical to help users target their reading interests and make personalized recommendations [2, 25, 27, 32, 34, 39].”
• MIND: A Large-scale Dataset for News Recommendation (2020, background): “Traditional news recommendation meth-ods usually rely on feature engineering to represent news articles and user interest (Liu et al., 2010; 3 https://microsoftnews.msn.com/ Son et al., 2013; Karkali et al., 2013; Garcin et al., 2013; Bansal et al., 2015; Chen et al., 2017).”
• Neural News Recommendation with Long- and Short-term User Representations (2019, background): “Many conventional news recommendation methods rely on manual feature engineering to build news and user representations (Phelan et al., 2009; Liu et al., 2010; Li et al., 2010; Son et al., 2013; Li et al., 2014; Bansal et al., 2015; Lian et al., 2018).”
• News recommender systems - Survey and roads ahead (2018, methodology): “[165] 2013 CB User Profiling New York Times Kappa Statistic, NDCG Offline”
• Neural News Recommendation with Attentive Multi-View Learning (2019, background): “Many existing news recommendation meth-ods rely on manual feature engineering for news and user representation learning [ Liu et al. , 2010; Son et al. , 2013; Bansal et al. , 2015 ] .”
• NPA: Neural News Recommendation with Personalized Attention (2019, methodology): “Thus, content-based techniques are usually complementary methods to CF [2, 20, 21, 24, 27, 29, 32, 39].”