EigenRank: a ranking-oriented approach to collaborative filtering

By Nathan Nan Liu, Qiang Yang. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2008

315 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/b3b7e1002192ef68cafbe45c84895f9e3caa393f

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

In Information Systems

Citations in the corpus

List-wise learning to rank with matrix factorization for collaborative filtering (2010, methodology): “Approaches to CF that are conceptually similar to pair-wise LTR have been presented in the literature [11][12][14], but a scalable solution to the complexity problem remains elusive.”
Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach (2020, methodology): “Some earlier works applied personalized random walks (Liu and Yang 2008) or relied on graph regularization models with auxiliary graph data (e.”
Collaborative competitive filtering: learning recommender using context of user choice (2011, background): “…of movies only tells us that the user appreciates action movies; however, a preference for Die Hard over Terminator or Rocky suggests that the user might favor Bruce Table 1: An example of user-recommender interactions and the derived observation matrix: entries with value “1” denote the action…”
QoS Ranking Prediction for Cloud Services (2013, methodology): “Liu and Yang [12] propose a ranking-oriented collaborative filtering approach to rank movies.”
TFMAP: optimizing MAP for top-n context-aware recommendation (2012, background): “Although ranking-oriented CF approaches have been proposed for explicit feedback domains, e.g., EigenRank [19] and CoFiRank [34], those approaches are di.cult to apply to implicit feedback domains, since they require training examples that are derived from the users ratings on various items.”
Recommender systems with social regularization (2011, methodology): “Algorithms in this category include the clustering model [13], the aspect models [8, 9, 33], the latent factor model [5], the Bayesian hierarchical model [40] and the ranking model [19].”
Probabilistic factor models for web site recommendation (2011, methodology): “Algorithms in this category include the clustering model [12], the aspect models [7, 8, 25], the latent factor model [3], the Bayesian hierarchical model [31] and the ranking model [18].”
Temporal recommendation on graphs via long- and short-term preference fusion (2010, background): “erences on items, [3, 15, 12, 7] proposed their personalized random walk models respectively to make recommendation in different domains.”
CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering (2012, methodology): “A ranking-oriented CF that extends memory-based (or similarity-based) approaches has been proposed in EigenRank [16].”
Music recommendation by unified hypergraph: combining social media information and music content (2010, unknown): “Traditional recommendation algorithms, such as Collaborative Filtering (CF ) [29, 16, 21], only consider the user-item rating matrix and fail to take advantage of other kinds of social media information.”
Factorization Machines with libFM (2012, methodology): “Ranking LIBFM also contains methods for optimizing an FM model with respect to ranking [Liu and Yang 2008] based on pairwise classi.cation [Rendle et al. 2009].”
Learning to recommend with social trust ensemble (2009, methodology): “Examples of model-based approaches include aspect models [7, 8, 21], the latent factor model [4], the Bayesian hierarchical model [24] and the ranking model [11].”
Current challenges and visions in music recommender systems research (2017, background): “It is nowadays also frequently used for evaluating music recommender systems [120, 139, 185].”
Interactive collaborative filtering (2013, unknown): “In addition, we add a constraint for all the algorithms that the same item should not be repeatedly recommended as suggested in most previous ranking-oriented recommendation settings [28, 40].”
Recommender Systems (2016, unknown): “Other well-known models used to learn these predictions include the Bayesian personalized ranking model (BPR) [499], EigenRank model [367], pLPA [368], and CR [59].”