Tag recommendations based on tensor dimensionality reduction

By Panagiotis Symeonidis, Alexandros Nanopoulos, Yannis Manolopoulos. ACM Conference on Recommender Systems, 2008

344 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mathematics, Information Systems

View Paper: https://www.semanticscholar.org/paper/b23f05414591fbc7b979eacaa4452343e8907c64

How researchers see this paper

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

In Artificial Intelligence

1. Yongfeng Zhang
2. Xu Chen
3. Zheng Qin
4. Xiangnan He
5. Lars Schmidt-Thieme
6. Steffen Rendle
7. Yue Shi
8. Alan Hanjalic

In Computational Mathematics

1. Steffen Rendle
2. Lars Schmidt-Thieme
3. Yue Shi
4. Martha A. Larson
5. Alan Hanjalic
6. Yongfeng Zhang
7. Xu Chen
8. Zheng Qin

In Information Systems

1. Yongfeng Zhang
2. Xu Chen
3. Xiangnan He
4. Zheng Qin
5. Steffen Rendle
6. Yue Shi
7. Lars Schmidt-Thieme
8. Martha A. Larson

Citations in the corpus

• Learning to Rank Features for Recommendation over Multiple Categories (2016, methodology): “A number of techniques exist for tensor factorization [15, 16, 13], and we adopt a method similar to [15] due to its efficiency and relatively lower learning complexity.”
• Learning optimal ranking with tensor factorization for tag recommendation (2009, methodology): “For tag recommendation the missing values are usually filled with zeros [14].”
• Content-based Recommender Systems: State of the Art and Trends (2011, neutral): “assisting users for annotation purposes) [97, 94].”
• A New Truncation Strategy for the Higher-Order Singular Value Decomposition (2012, methodology): “The HOSVD has been applied in numerous application domains [29], such as image processing [9, 32, 39, 61], pattern recognition [49, 50, 59, 60, 62], data mining [33, 34, 52, 53], signal processing [12, 19, 36, 37, 38, 45], psychometrics [54, 55, 56], chemometrics [5], and biomedicine [16, 40, 41].”
• Recommender Systems (2016, neutral): “Tensor-based methods for tag recommendation are presented in [497, 498, 582, 583].”
• Collaborative Filtering beyond the User-Item Matrix (2014, methodology): “…webpage, click (boolean)] data is modeled recommender systems have exploited the Tucker TF model for processing [user, item, tag, usage (boolean)] data for the purpose of either item recommendation [Xu et al. 2006], or tag recommendation [Symeonidis et al. 2008b], or both [Symeonidis et al. 2010].”
• Latent dirichlet allocation for tag recommendation (2009, methodology): “Whereas [20] operate on the URT graph directly, [30] use generalized techniques of SVD (Singular Value Decomposition) for n-dimensional tensors.”
• Pairwise interaction tensor factorization for personalized tag recommendation (2010, methodology): “In [22] a Higher-Order-Singular-Value-Decomposition (HOSVD) is used – which corresponds to a TD model optimized for square-loss where all not observed values are learned as 0s.”