Collaborative competitive filtering: learning recommender using context of user choice

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

159 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research, Marketing, 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 Information Systems

In Management Science and Operations Research

In Marketing

In Recommender Systems

Citations in the corpus

Variational Autoencoders for Collaborative Filtering (2018, background): “nomial likelihood is less well studied in the context of latent-factor models such as matrix factorization and autoencoders. A notable exception is the collaborative competitive filtering (CCF) model [53] and its successors, which take advantage of more fine-grained information about what options we”
Neural Survival Recommender (2017, background): “Notably due to the Netﬂix prize [2], factorization-based models have been popular even after a decade of its ﬁrst development, and a lot of the eﬀort has been put into improving the performance under various circumstances [1, 23, 34, 35, 15, 21].”
TFMAP: optimizing MAP for top-n context-aware recommendation (2012, background): “Finally, note that a recommended item set from a recommender is regarded as the “context” of user choice in the work of [38].”
The FacT: Taming Latent Factor Models for Explainability with Factorization Trees (2019, background): “n error over a set of observed ratings O = {(i,j)|rij isobserved}as follows, L(U,V,O)= min U,V Õ (i,j)∈O (rij −u⊤ i vj) 2. (1) It is well accepted that recommendation is essentially a ranking problem [22, 37]; however, the objective function introduced in Eq (1) cannot fully characterize the need of”
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].”
Collaborative Denoising Auto-Encoders for Top-N Recommender Systems (2016, unknown): “, [15, 31, 7]).”
Transfer Learning for Semisupervised Collaborative Recommendation (2016, methodology): “2009] and Competitive Collaborative Filtering (CCF) [Yang et al. 2011].”
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence GBPR: Group Preference Based Bayesian Personalized Ranking for One-Class Collaborative Filtering (unknown): “…[Du et al. , 2011] than pointwise methods [Pan et al. , 2008; Hu et al. , 2008], and has been successfully adopted in many applications, e.g., tag recommendation [Rendle and Schmidt-Thieme, 2010], news recommendation [Yang et al. , 2011], and shopping items recommendation [Kanagal et al. , 2012].”
CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering (2012, methodology): “The key constraint introduced in CCF is that the utility (or relevance) of a rated item should be higher than any items that are in the candidate set but not rated/selected.”
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 .”
Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS) (2014, methodology): “One may argue that this is only part of a solution, since the relative values of the entries matter more than their absolute value [25, 24].”