Building and applying a concept hierarchy representation of a user profile

Nanas, Nikolaos; Uren, Victoria; Roeck, Anne De

doi:10.1145/860472.860473

Cited by 14 publications

(13 citation statements)

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“…There are many alternative weighting methods such as Relative Document Frequency (RelDF) [26] and Residual Inverse Document Frequency (RIDF) [24]. RelDF is defined as the difference between the probability of a term appearing in a relevant document and the probability of it appearing in a random document.…”

Section: Discussionmentioning

confidence: 99%

Multivariable stream data classification using motifs and their temporal relations

Seo

Kang

Ryu

2009

Information Sciences

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Section: Discussionmentioning

confidence: 99%

Multivariable stream data classification using motifs and their temporal relations

Seo

Kang

Ryu

2009

Information Sciences

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“…Context here is defined by a sliding window, that is, a span of contiguous terms that slides through a document's text (punctuation marks are ignored). The assumption here is that terms which appear close to each other in text can be semantically or syntactically correlated (Nanas et al 2003). Here we have experimented with a window equal to ten contiguous terms which has produced the best results after experiments comparing various window sizes (Nanas and Vavalis 2008).…”

Section: Term Correlationsmentioning

confidence: 99%

“…We then deploy the method described in detail in Nanas et al (2003) and evaluated in Nanas et al (2004c), to assign a weight in the interval (−1, 1) to the remaining stemmed words in the document and we extract those with weight over a specified threshold. 4 Some of the extracted terms may already appear in the profile, with the rest being new terms.…”

Section: Adaptation Through Self-organisationmentioning

confidence: 99%

“…A small variation of the TREC routing guidelines was initially used to compare the first version of Nootropia to a keyword vector profile containing the same weighted terms but no links (Nanas et al 2003). The results indicate that as the number of terms in the profile increases Nootropia becomes better than the corresponding vector representation.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…In Nanas et al (2003Nanas et al ( , 2004b, Nanas and Vavalis (2008), we have compared Nootropia to keyword vector profiles with positive results. However, the observed differences in performance have not been significant.…”

Section: Introductionmentioning

confidence: 99%

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2010

Self Cite

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Jerne's idiotypic network theory stresses the importance of antibody-to-antibody interactions and provides possible explanations for self-tolerance and increased diversity in the immune repertoire. In this paper, we use an immune network model to build a user profile for adaptive information filtering. Antibody-to-antibody interactions in the profile's network model correlations between words in text. The user profile has to be able to represent a user's multiple interests and adapt to changes in them over time. This is a complex and dynamic engineering problem with clear analogies to the immune process of self-assertion. We present a series of experiments investigating the effect of term correlations on the user's profile performance. The results show that term correlations can encode additional information, which has a positive effect on the profile's ability to assess the relevance of documents to the user's interests and to adapt to changes in them.

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Detect structural‐connected communities based on BSCHEF in C‐DBLP

Rong

Ying³

et al. 2015

Concurrency and Computation

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Chinese Digital Bibliography & Library Project (C-DBLP) is a huge and real-life co-author social network in China, rarely cited by published paper. It contains a large amount of ground-truth community structure with distinguished research topics. Despite the fact that rich studies on community detection have been conducted with gains of practically fruitful algorithms, unfortunately, with the coming of 'Big Data' era and speedy development of mobile devices, social networks like C-DBLP have incredibly expanded on nodes and edges, as a result, because of massive data cardinality, a large portion of community detection methods consume memory resource excessively. Therefore, in this work, we select Based on Structural Connection Hierarchical Exploration (BSCHE) algorithm to partition nodes in C-DBLP because of its O(n) time cost, fast enough to process massive data, and its novel physical meaning of similarity between nodes defined by structural connection and availability. In addition, in order to avoid huge memory resource consumption caused by 'Big Data' of C-DBLP, we strengthen BSCHE as a framework (BSCHEF) by our proposed 'countpointer-strategy' imitated from incremental batch process to detect co-author communities on C-DBLP. The experiment results show that BSCHEF can find sets of communities on C-DBLP more effectively with the highest modularity value and the least execution time compared to other clustering algorithm.

show abstract

Building and applying a concept hierarchy representation of a user profile

Cited by 14 publications

References 0 publications

Multivariable stream data classification using motifs and their temporal relations

Multivariable stream data classification using motifs and their temporal relations

Words, antibodies and their interactions

Detect structural‐connected communities based on BSCHEF in C‐DBLP

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