Like like alike — Joint Friendship and Interest Propagation in Social Networks Shuang

By Shuang-Hong Yang, Bo Long, Alex Smola, Narayanan Sadagopan, Zhaohui Zheng, Hongyuan Zha. 2011

Subfields: Graph Learning, Recommender Systems, Social Sciences & Humanities

How researchers see this paper

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

In Graph Learning

1. Kevin Chen-Chuan Chang
2. Peng Cui
3. Wenwu Zhu
4. Charu C. Aggarwal
5. Lars Schmidt-Thieme
6. Artus Krohn-Grimberghe
7. Prateek Mittal
8. Dawn Song

In Recommender Systems

1. Julian McAuley
2. Charu C. Aggarwal
3. Qiang Yang
4. Yue Shi
5. Xiangnan He
6. George Karypis
7. Houfeng Wang
8. Qing Zhang

In Social Sciences & Humanities

1. Peng Cui
2. Hongyuan Zha
3. Fernando Diaz
4. Wenwu Zhu
5. Carlos Castillo
6. Maarten de Rijke
7. Charles Peter Elkan
8. Aditya Krishna Menon

Citations in the corpus

• Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Hence, it is important for guiding the design of cross-cultural tools (Hong et al., 2011; Yang et al., 2011), and for understanding socio-economic phenomena (Garcia-Gavilanes et al., 2013).”
• Evolution of social-attribute networks: measurements, modeling, and implications using google+ (2012, background): “Recent studies have demonstrated the promise of social-attribute networks in applications such as link prediction [58, 17], attribute inference [17, 58], and community detection [62].”
• Sparse linear methods with side information for top-n recommendations (2012, background): “Yang et al [16] developed a joint friendship and interest propagation model for top-N recommendation, in which the user-item interest network and the user-user friendship network (side information on users) are jointly modeled through latent user and item factors.”
• Joint Link Prediction and Attribute Inference Using a Social-Attribute Network (2014, background): “Yang et al. [2011] proposed jointly predicting links and propagating node interests (e.g., music interest).”
• Not All Links Are Created Equal: An Adaptive Embedding Approach for Social Personalized Ranking (2016, background): “The most similar work to this paper is [10], which proposed a model with joint friendship and interest propagation in social network.”
• Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering (2014, unknown): “Copyright 2014 ACM 978-1-4503-2598-1/14/11 ...$15.00. http://dx.doi.org/10.1145/2661829.2661998 .”
• Mining Text Data (2012, methodology): “combined an entity kernel with the convolution tree kernel to form a composite kernel [69].”
• Recommender Systems (2012, background): “Secondly, the friendship network and interest-based network are strongly correlated with each other [241], and friends tend to visit the same items and vote them with similar ratings [242].”
• Link Prediction via Matrix Factorization (2011, methodology): “In the machine learning literature, computationally efficient frequentist versions of these latent feature models have been studied in [23,37], and Bayesian models have also been extended to allow for an infinite number of latent features [24,25].”
• Collaborative Filtering beyond the User-Item Matrix (2014, background): “One recent contribution has exploited the information of user choice in recommendation sessions for model training [Yang et al. 2011b].”
• Towards social user profiling: unified and discriminative influence model for inferring home locations (2012, background): “[18] propose a model to propagate interests of an item among users via their friendships.”
• Transformer Hawkes Process (2020, background): “Through social media such as Twitter and Facebook, we share our experiences and respond to other users’ information (Yang et al., 2011).”
• Social Collaborative Viewpoint Regression with Explainable Recommendations (2017, background): “[51] address recommendation and link prediction tasks based on a joint-propagation model between social relations and interests.”
• Collaborative competitive filtering: learning recommender using context of user choice (2011, background): “We then assume the utility r ui as a function of both types of features (i.e. observable and latent) [28]: where the matrix M ∈ R m × n provides a bilinear form for characterizing the utility based on the content features of the corresponding dyads.”
• Collaborative Filtering-Based Recommendation of Online Social Voting (2017, methodology): “…OSNs now offer the social voting function, through which a user can share with friends her opinions, e.g., like or dislike, on various subjects, ranging from user statuses, profile pictures, to games played, Manuscript received October 22, 2014; revised August 19, 2016; accepted January 24, 2017.”