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Output details

15 - General Engineering

Brunel University London

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Output 215 of 258 in the submission

Article title

Semantic content ranking through collaborative and content clustering

Type

D - Journal article

DOI

10.1016/j.neucom.2007.10.029

Title of journal

Neurocomputing

Article number

Volume number

Issue number

13-15

First page of article

2587

ISSN of journal

09252312

Year of publication

2008

URL

http://pdn.sciencedirect.com/science?_ob=MiamiImageURL&_cid=271597&_user=545641&_pii=S092523120800221X&_check=y&_origin=article&_zone=toolbar&_coverDate=31-Aug-2008&view=c&originContentFamily=serial&wchp=dGLbVlt-zSkWz&md5=d7dc3c198316a224fff2e99983f384fc&p

Number of additional authors

Additional information

This paper reports on our early attempts at exploiting evolutionary computation techniques in semantic content modelling and, in particular, in enhancing our EPSRC-funded semantic content modelling system, COSMOS-7. Until then, when queried, COSMOS-7’s output was a sequence of relevant yet unranked video segments which users have had to sift through. Using Self-Organising Neural Networks, we developed an add-on module that clusters and ranks COSMOS-7 output through consideration of user preferences and knowledge gained from usage of the same content by similar users and similar content by the same user. The research was funded through EPSRC doctoral training accounts (EP/P501334/1, GR/P01496/01).

Interdisciplinary

Cross-referral requested

Research group

None

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract