CubeSVD: a novel approach to personalized Web search

By Hua-Jun Zeng, Huan Liu, Yuchang Lu, Zheng Chen. The Web Conference, 2005

393 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mathematics, Information Systems

View Paper: https://www.semanticscholar.org/paper/158415ae990b78023a3e1af851c418a94da91325

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Citations in the corpus

Scalable Tensor Decompositions for Multi-aspect Data Mining (2008, methodology): “As a higher-order generalization of SVD and PCA, Tucker decomposition has widely been used as a core technique for analyzing sparse tensors [19, 20, 8].”
GigaTensor: scaling tensor analysis up by 100 times - algorithms and discoveries (2012, neutral): “In [31], Sun et al.”
Unsupervised Multiway Data Analysis: A Literature Survey (2009, methodology): “N-mode SVD in [38] and [39], or CubeSVD in [50].”
Tag recommendations based on tensor dimensionality reduction (2008, neutral): “proposed CubeSVD [15] to improve Web Search.”
Temporal QoS-aware web service recommendation via non-negative tensor factorization (2014, methodology): “Tensor factorization is an important technique in many applications, such as data mining, dimensionality reduction, chemometrics, signal processing, neuroscience, and web analysis [7, 10, 26, 8].”
Robust Low-Rank Tensor Recovery: Models and Algorithms (2013, methodology): “Having originated in the ﬁelds of psychometrics and chemometrics, these decompositions are now widely used in other application areas such as computer vision [49], web data mining [18, 42], and signal processing [1].”
Probabilistic Models for Incomplete Multi-dimensional Arrays (2009, methodology): “, 2007) on microarray analysis, (Wang & Ahuja, 2005) on image ensemble and (Sun et al., 2005) on personalized search.”
MACH: Fast Randomized Tensor Decompositions (2009, background): “Indicatively, computer vision and signal processing (e.g., [51, 35, 43]), neuroscience (e.g., [5]), time series anomaly detection (e.g., [47]), psychometrics (e.g., [49]), chemometrics (e.g., [44]), graph analysis (e.g., [25, 45]), data mining (e.g., [ 48 ]).”
Measuring personalization of web search (2013, background): “Personalized search has been extensively studied in the literature [8, 19, 23, 27, 29, 36, 39, 43].”
A large-scale evaluation and analysis of personalized search strategies (2007, methodology): “Collaborative filtering is a typical group-based personalization method and has been used in personalized search in [29] and [30].”
Automatic identification of user interest for personalized search (2006, background): “[18] does a sophisticated analysis on the correlation between users, their queries and search results clicked to model user preferences, but due to the complexity of the analysis, we believe this method is difficult to scale to general search engines.”
Tensor Decompositions and Applications (2009, methodology): “[211] used Tucker to analyze web site click-through data.”
Tensors for Data Mining and Data Fusion (2016, methodology): “Sun et al. [2005] personalize web search by using historic click-through data of users.”
Higher-order Web link analysis using multilinear algebra (2005, background): “Currently, we are studying an alternative decomposition to PARAFAC called the Tucker model [36] for applications in information retrieval.”
A Generic Coordinate Descent Framework for Learning from Implicit Feedback (2016, background): “Additionally, we want to highlight, that existing tensor factorization learning algorithms [19, 20, 10] require that the tensor data is dense, i.e., the empty parts in the tensor in Figure 4 are ﬁlled with zeros.”
Collaborative Filtering beyond the User-Item Matrix (2014, methodology): “Inspired by the work on personalized Web search [Sun et al. 2005], where the [user, query, webpage, click (boolean)] data is modeled by Tucker TF, researchers in recommender systems have exploited the Tucker TF model for processing [user, item, tag, usage (boolean)] data for the purpose of either it”
KDD CUP-2005 report: facing a great challenge (2005, background): “Given the exponential growth of information s availability in electronic form, search becomes one of the most important and effective approaches to finding correct/relevant information to serve our needs.”
Multilinear operators for higher-order decompositions (2006, background): “Higher-order tensor decompositions are in frequent use today in a variety of fields including psychometrics [43, 12, 19], chemometrics [6, 37], image analysis [45, 36, 46], graph analysis [27, 26], signal processing [13, 33], and much more [1, 38, 32].”