Google news personalization: scalable online collaborative filtering

By Abhinandan Das, Mayur Datar, Ashutosh Garg, Shyamsundar Rajaram. 2007

1,820 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Recommender 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 Vision and Pattern Recognition

1. Dacheng Tao
2. Luc Van Gool
3. Trevor Darrell
4. Xiaogang Wang
5. Ming-Hsuan Yang
6. Yi Yang
7. Jitendra Malik
8. Bernt Schiele

In Information Systems

1. Xiangnan He
2. Xing Xie
3. Tat-seng Chua
4. Charu C. Aggarwal
5. Julian McAuley
6. Yongfeng Zhang
7. Xiang Wang
8. Qiang Yang

In Recommender Systems

1. Julian McAuley
2. Xiangnan He
3. Charu C. Aggarwal
4. Ruining He
5. Xing Xie
6. Tat-seng Chua
7. Lihong Li
8. Qingyun Wu

Citations in the corpus

• VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback (2015, background): “…items of personal interest from huge corpora, ranging from movies and music (Bennett and Lanning, 2007; Koenigstein, Dror, and Koren, 2011), to research articles, news and books (Das et al., 2007; Lu et al., 2015), to tags and even other users (Xu et al., 2011; Zhou et al., 2010; Zhu et al., 2011).”
• DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018, methodology): “Personalized news recommendation has attracted a lot of attention in recent years [11, 23, 45].”
• Why Amazon's Ratings Might Mislead You: The Story of Herding Effects (2014, neutral): “Indeed, models on collective intelligence, from majority voting to collaborating filtering 9–12 and crowdsourcing, 13,14 all assume independent crowds, representing a critical gap between modeling frameworks and empirical insights.”
• Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time (2017, background): “Other examples of real-time recommendation systems include news recommendations [9, 26].”
• A contextual-bandit approach to personalized news article recommendation (2010, background): “. service vendors acquire and maintain a large amount of content in their repository, for instance, for filtering news articles [14] or for the display of advertisements [5].”
• Efficient Interaction-based Neural Ranking with Locality Sensitive Hashing (2019, methodology): “Our work is motivated by the rich history of research on approximation with hashing such as LSH [1–4, 7, 8, 13, 15, 37] and in various data-intensive applications [6, 10, 17, 18, 30].”
• Social influence analysis in large-scale networks (2009, methodology): “, news personalization with Map-Reduce EM algorithm [8], Map-Reduce of several machine learning algorithms on multicore architecture [5].”
• Content-Based News Recommendation (2010, background): “Also the combination of various approaches for every type is possible e.g. Google News [5].”
• Divide-and-Conquer Matrix Factorization (2011, background): “…applications, including collaborative filtering for recommender systems (e.g., [19] and the references therein), link prediction for social networks [15], click prediction for web search [6], video surveillance [2], graphical model selection [4], document modeling [27], and image alignment [33].”
• Style in the long tail: discovering unique interests with latent variable models in large scale social E-commerce (2014, background): “In addition to commerce applications, recommender systems appear in a variety of other settings, for instance, recommending news articles to web surfers [5].”
• Terms of a Feather: Content-Based News Recommendation and Discovery Using Twitter (2011, background): “[5] mostly describe scalability of the system as an issue with the service, and propose several techniques know to be capable of dealing with such issues.”
• Asymmetric Minwise Hashing for Indexing Binary Inner Products and Set Containment (2015, background): “Later, because of its locality sensitive property [22], minhash became a widely used hash function for creating hash buckets leading to efficient algorithms for numerous applications including spam detection [6], collaborative filtering [4], news personalization [15], compressing social networks [13”
• Explainable Recommendation through Attentive Multi-View Learning (2019, methodology): “Many methods have been proposed to improve recommendation accuracy, including content-based (Kompan and Bielikov ´ a 2010), collaborative filtering-based (Das et al. 2007) and hybrid methods (De Francisci Morales, Gionis, and Lucchese 2012).”
• A Meta-Learning Perspective on Cold-Start Recommendations for Items (2017, background): “Matrix factorization (MF) is the most popular instantiation of latent factor models and has been used for large scale recommendations of products [19], movies [15]) and news [7].”