Interpreting TF-IDF term weights as making relevance decisions

Wu, H. C.; Luk, Robert W. P.; Wong, Kam-Fai; Kwok, K. L.

doi:10.1145/1361684.1361686

Cited by 635 publications

(323 citation statements)

References 69 publications

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“…Most of the content-based approaches focus on items which contain textual information such as news, books and other documents [15], [16]. Mooney et al [17] developed a book recommending system that utilizes semi-structured information about items gathered from the web using simple information extraction techniques.…”

Section: Related Workmentioning

confidence: 99%

Using Trust of Social Ties for Recommendation

Chen

Shao

Zhu

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYNowadays, with the development of online social networks (OSN), a mass of online social information has been generated in OSN, which has triggered research on social recommendation. Collaborative filtering, as one of the most popular techniques in social recommendation, faces several challenges, such as data sparsity, cold-start users and prediction quality. The motivation of our work is to deal with the above challenges by effectively combining collaborative filtering technology with social information. The trust relationship has been identified as a useful means of using social information to improve the quality of recommendation. In this paper, we propose a trust-based recommendation approach which uses GlobalTrust (GT) to represent the trust value among users as neighboring nodes. A matrix factorization based on singular value decomposition is used to get a trust network built on the GT value. The recommendation results are obtained through a modified random walk algorithm called GlobalTrustWalker. Through experiments on a real-world sparser dataset, we demonstrate that the proposed approach can better utilize users' social trust information and improve the recommendation accuracy on coldstart users. key words: social network, trust-based, collaborative filtering, random walk

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Section: Related Workmentioning

confidence: 99%

Using Trust of Social Ties for Recommendation

Chen

Shao

Zhu

et al. 2016

IEICE Trans. Inf. & Syst.

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“…As for why we are using only one artificial "addition" the same observation as in Case 2 stands. In this case the number of artificially added negative data points is one (δn A i = 1), so (5) can be transformed as (11):…”

Section: Calculation Of Weight Of Evidence For Binary Problems Whmentioning

confidence: 99%

“…The term frequency-inverse document frequency (TF-IDF), as described in [10] and [11], is often used in text mining problems as a numerical statistic which estimates the importance of a word to a document in a collection of documents. In a similar manner this weight can be used to transform arbitrary nominal values into numerical just as it assigns weight to words in text mining and information retrieval.…”

Section: Introductionmentioning

confidence: 99%

Transformation of nominal features into numeric in supervised multi-class problems based on the weight of evidence parameter

Zdravevski¹,

Lameski²,

Kulakov³

et al. 2015

Annals of Computer Science and Information Systems

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Abstract-Machine learning has received increased interest by both the scientific community and the industry. Most of the machine learning algorithms rely on certain distance metrics that can only be applied to numeric data. This becomes a problem in complex datasets that contain heterogeneous data consisted of numeric and nominal (i.e. categorical) features. Thus the need of transformation from nominal to numeric data. Weight of evidence (WoE) is one of the parameters that can be used for transformation of the nominal features to numeric. In this paper we describe a method that uses WoE to transform the features. Although the applicability of this method is researched to some extent, in this paper we extend its applicability for multi-class problems, which is a novelty. We compared it with the method that generates dummy features. We test both methods on binary and multi-class classification problems with different machine learning algorithms. Our experiments show that the WoE based transformation generates smaller number of features compared to the technique based on generation of dummy features while also improving the classification accuracy, reducing memory complexity and shortening the execution time. Be that as it may, we also point out some of its weaknesses and make some recommendations when to use the method based on dummy features generation instead.

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“…The application of both techniques to the body of the reports allows to extract the main clues in key-value pairs. Relevance of clues in each case is estimated in the next module, the "Clues relevance estimation", using relevance algorithm such as tf-idf [16]. The information at this point is expressed as:…”

Section: Fig 2 Clues Recommendation Algorithmmentioning

confidence: 99%

Recommendation techniques in forensic data analysis: a new approach

Quintana

Uribe

Sánchez

et al. 2015

6th International Conference on Imaging for Crime Prevention and Detection (ICDP-15)

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Data mining for digital forensic analysis is a branch of Computer Science focused on pattern extraction from largescale data which has been used to support analysts when trying to solve crimes. One of the most promising applications of data mining algorithms is to build recommendation systems, aiming to propose future directions to the investigation and to guide the analyst through the process. In this paper we propose a new approach, architecture and framework with the purpose of taking advantage of the recommender systems techniques to the forensic field and provide examples of their applicability to different use cases involving large scale collections of multimedia information related to a defined forensic case.

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Interpreting TF-IDF term weights as making relevance decisions

Cited by 635 publications

References 69 publications

Using Trust of Social Ties for Recommendation

Using Trust of Social Ties for Recommendation

Transformation of nominal features into numeric in supervised multi-class problems based on the weight of evidence parameter

Recommendation techniques in forensic data analysis: a new approach

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