Extracting skill endorsements from personal communication data

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Mallenahalli Shankara Lingappa, Darshan
dc.contributor.author De Fransisci Morales, Gianmarco
dc.contributor.author Gionis, Aristides
dc.date.accessioned 2018-09-06T10:16:39Z
dc.date.available 2018-09-06T10:16:39Z
dc.date.issued 2016-10-24
dc.identifier.citation Mallenahalli Shankara Lingappa , D , De Fransisci Morales , G & Gionis , A 2016 , Extracting skill endorsements from personal communication data . in CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management . vol. 24-28-October-2016 , ACM , pp. 1961-1964 , ACM International Conference on Information and Knowledge Management , Indianapolis , United States , 24/10/2016 . https://doi.org/10.1145/2983323.2983884 en
dc.identifier.isbn 9781450340731
dc.identifier.other PURE UUID: 6af8d53c-a236-47a5-b76e-c0698426c6eb
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/extracting-skill-endorsements-from-personal-communication-data(6af8d53c-a236-47a5-b76e-c0698426c6eb).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=84996538828&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/26625829/skills.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/33861
dc.description | openaire: EC/H2020/654024/EU//SoBigData
dc.description.abstract People are increasingly communicating and collaborating via digital platforms, such as email and messaging applications. Data exchanged on these digital communication platforms can be a treasure trove of information on people who participate in the discussions: who they are collaborating with, what they are working on, what their expertise is, and so on. Yet, personal communication data is very rarely analyzed due to the sensitivity of the information it contains. In this paper, we mine personal communication data with the goal of generating skill endorsements of the type "person A endorses person B on skill X." To address privacy concerns, we consider that each person has access only to their own data (i.e., conversations with their peers). By using our method, they can generate endorsements for their peers, which they can inspect and opt to publish. To identify meaningful skills we use a knowledge base created from the StackExchange q&a forum. We study two different approaches, one based on building a skillgraph, and one based on information retrieval techniques. We find that the latter approach outperforms the graph-based algorithms when tested on a dataset of user profiles from StackOverflow. We also conduct a user study on email data and find that the information retrieval-based approach achieves a MAP@10 score of 0.617. en
dc.format.extent 4
dc.format.extent 1961-1964
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation info:eu-repo/grantAgreement/EC/H2020/654024/EU//SoBigData
dc.relation.ispartof ACM International Conference on Information & Knowledge Management en
dc.relation.ispartofseries CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management en
dc.relation.ispartofseries Volume 24-28-October-2016 en
dc.rights openAccess en
dc.subject.other Business, Management and Accounting(all) en
dc.subject.other Decision Sciences(all) en
dc.subject.other 113 Computer and information sciences en
dc.title Extracting skill endorsements from personal communication data en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science
dc.contributor.department Qatar Computing Research Institute
dc.subject.keyword E-mail mining
dc.subject.keyword Personal data
dc.subject.keyword Skill endorsements
dc.subject.keyword Business, Management and Accounting(all)
dc.subject.keyword Decision Sciences(all)
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201809064972
dc.identifier.doi 10.1145/2983323.2983884
dc.type.version acceptedVersion

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