Information Retrieval using a Singular Value Decomposition Model of Latent Semantic Structure

Furnas, George W.; Deerwester, Scott; Durnais, Susan T.; Landauer, Thomas K.; Harshman, Richard A.; Streeter, Lynn A.; Lochbaum, Karen E.

doi:10.1145/3130348.3130358

Cited by 132 publications

(64 citation statements)

References 13 publications

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“…A number of techniques have been developed to fulfill these requirements for the MF case. [26][27][28] As shown in Social Tagging section, these ideas can be also applied in the TF case.…”

Section: Real-time Recommendationsmentioning

confidence: 99%

“…In order to deal with the problem of real-time recommendations (see Real-Time Recommendations section) the authors adopt a well-known folding-in method 27 to a higher order case. The folding-in procedure helps to quickly embed a previously unseen entity into the latent features space without recomputing the whole model.…”

Section: Unified Frameworkmentioning

confidence: 99%

“…Note, that MAP optimization also has a weighted form due to Eq. (27); however, the computation complexity would still be prohibitively high due to its complex structure. In order to mitigate that, the authors propose the fast learning algorithm: for each (user and context) pair only a limited set of a representative items (a buffer) is considered, which in turn, allows to control the computational complexity.…”

Section: Tfmapmentioning

confidence: 99%

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Tensor methods and recommender systems

Frolov

Oseledets

2017

WIREs Data Min & Knowl

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A substantial progress in development of new and efficient tensor factorization techniques has led to an extensive research of their applicability in recommender systems field. Tensor-based recommender models push the boundaries of traditional collaborative filtering techniques by taking into account a multifaceted nature of real environments, which allows to produce more accurate, situational (e.g. context-aware, criteria-driven) recommendations. Despite the promising results, tensor-based methods are poorly covered in existing recommender systems surveys. This survey aims to complement previous works and provide a comprehensive overview on the subject. To the best of our knowledge, this is the first attempt to consolidate studies from various application domains, which helps to get a notion of the current state of the field. We also provide a high level discussion of the future perspectives and directions for further improvement of tensor-based recommendation systems.

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“…A number of techniques have been developed to fulfill these requirements for the MF case. [26][27][28] As shown in Social Tagging section, these ideas can be also applied in the TF case.…”

Section: Real-time Recommendationsmentioning

confidence: 99%

Section: Unified Frameworkmentioning

confidence: 99%

Section: Tfmapmentioning

confidence: 99%

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Tensor methods and recommender systems

Frolov

Oseledets

2017

WIREs Data Min & Knowl

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“…Singular Value Decomposition (SVD) [5] has been used as an effective method for document categorization [24]. In this section, we apply this method for database categorization.…”

Section: Singular Value Decomposition (Svd)mentioning

confidence: 99%

Concept hierarchy based text database categorization in a metasearch engine environment

Wang

Meng

Proceedings of the First International Conference on Web Information Systems Engineering

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Document categorization as a technique to improve the retrieval of useful documents has been extensively investigated. One important issue in a large-scale metasearch engine is to select text databases that are likely to contain useful documents f o r a given query. We believe that database categorization can be a potentially effective technique f o r good database selection, especially in the Internet environment where short queries are usually submitted. In this paper, we propose and evaluate several database categorization algorithms. This study indicates that while some document categorization algorithms could be adopted f o r database categorization, algorithms that take into consideration the special characteristics of databases may be more effective. Preliminary experimental results are provided to compare the proposed database categorization algorithms.

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“…Macedo et al have developed services [17] that automatically identify links among homogeneous Web repositories using lexical matching, Latent Semantic Indexing [18] and integrating an open linkbase [11] to store the computed links. The infrastructure was redesigned for reuse in linking service called LinkDigger that also allowed user feedback [19], and used to built the WebMemex recommender service [12].…”

Section: Introductionmentioning

confidence: 99%

Linking Everyday Presentations through Context Information

Macedo¹,

Neto

Camacho-Guerrero

et al.

Third Latin American Web Congress (LA-WEB'2005)

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A relevant issue on linking services based on information retrieval techniques is how to define scopes that delimit homogeneous information so as to obtain good results. An interesting way to achieve this is to provide such scope delimitation by means of context information. This process of linking can be tailored according to contextual constraints explicitly provided by users. We propose linking services enhanced with context information captured from everyday presentations. We present the LinkDigger Context Service, which creates hyperlinks following information obtained from users. As a result, different hypertexts can be defined upon the same information.

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Information Retrieval using a Singular Value Decomposition Model of Latent Semantic Structure

Cited by 132 publications

References 13 publications

Tensor methods and recommender systems

Tensor methods and recommender systems

Concept hierarchy based text database categorization in a metasearch engine environment

Linking Everyday Presentations through Context Information

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