PRISENIT-A Probabilistic Search Recommendation Algorithm to Improve Search Efficiency for Network Intelligence and Troubleshooting

Mikel Zuzuarregui, Enda Fallon, Mingxue Wang, John Keeney, Paul Jacob

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

When searching for data in a telecommunications network management application, large search result sets are common. In order to refine the results to retrieve useful information existing systems normally require additional user intervention such as appending or removing a search keyword, adding a filter, grouping results, etc. This work proposes a Probabilistic Search Recommendation Algorithm to Improve Search Efficiency for Network Intelligence and Troubleshooting (PRISENIT). PRISENIT is a query-based recommendation algorithm intended to improve search efficiency and usability for telecommunication system management. PRISENIT is an extension of an item-based collaborative filtering algorithm. It uses correlation-based similarity and users' implicit feedback in order to improve search efficiency. It learns from previous experiences in order to optimize decision-making. Currently there exists no known query-based recommender adaptation mechanism for network management. Existing search engines use previous user searches to make a suggestion based on the keyword. PRISENIT not only considers search terms, it also considers the influence of filters and features in order to makes network searches more efficient as it removes the necessity for users to manually choose search features or search filters. Experimental results show that PRISENIT can improve user experience in a telecommunications management environment.

Original languageEnglish
Title of host publicationProceedings - UKSim-AMSS 17th International Conference on Computer Modelling and Simulation, UKSim 2015
EditorsRichard Cant, Alessandra Orsoni, Ismail Saad, David Al-Dabass, Zuwairie Ibrahim
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages355-360
Number of pages6
ISBN (Electronic)9781479987122
ISBN (Print)9781479987122
DOIs
Publication statusPublished - 23 Sep 2016
Event17th UKSim-AMSS International Conference on Computer Modelling and Simulation, UKSim 2015 - Cambridge, United Kingdom
Duration: 25 Mar 201527 Mar 2015

Publication series

NameProceedings - UKSim-AMSS 17th International Conference on Computer Modelling and Simulation, UKSim 2015

Conference

Conference17th UKSim-AMSS International Conference on Computer Modelling and Simulation, UKSim 2015
Country/TerritoryUnited Kingdom
CityCambridge
Period25/03/1527/03/15

Keywords

  • Network management
  • item-based collaborative filtering
  • query-based recommender
  • recommender system

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