A user profile modelling using social annotations

Mezghani, Manel; Zayani, Corinne Amel; Amous, Ikram; Gargouri, Faı̈ez

doi:10.1145/2187980.2188230

Cited by 46 publications

(32 citation statements)

References 19 publications

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“…In fact, every user could describe his opinion on the content of a resource with his own keywords. Tag-based user profile modelling, in an adaptation context, has been detailed in [21].…”

Section: Interest Detection From Tagsmentioning

confidence: 99%

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Producing relevant interests from social networks by mining users' tagging behaviour: A first step towards adapting social information

Mezghani

Pninou

Zayani

et al. 2017

Data & Knowledge Engineering

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“…In fact, every user could describe his opinion on the content of a resource with his own keywords. Tag-based user profile modelling, in an adaptation context, has been detailed in [21].…”

Section: Interest Detection From Tagsmentioning

confidence: 99%

“…4 These techniques aim to structure the folksonomy in a comprehensible way to use it a recommendation, personalized, etc., purpose. More details about treating tag's ambiguity are explained in [5,21].…”

Section: Interest Detection From Tagsmentioning

confidence: 99%

Producing relevant interests from social networks by mining users' tagging behaviour: A first step towards adapting social information

Mezghani

Pninou

Zayani

et al. 2017

Data & Knowledge Engineering

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“…A tag is defined as a keyword generated by the user himself. Researches on updating a tag-based profile are already studied in our previous works [22]. The enrichment is used in a recommendation and crosssystem context [22].…”

Section: A Techniques For Updating the User Profilementioning

confidence: 99%

Dynamic enrichment of social users' interests

Mezghani¹,

Zayani²,

Amous³

et al. 2014

2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS)

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 13109 Abstract-In a social context, the user is more and more an active contributor for producing social information. Then, he needs a tailored information reflecting his current needs and interests in every period of time. This aims to provide a better adaptation while accessing the information space by integrating users' interests dynamic. Indeed, users' interests may change and become "outdated" through time. So, an interest judged as relevant in a period of time may fluctuate in the next period of time. Moreover, analysing the classic user behaviour to deduce his current interests is a difficult task. In fact, his behaviour isn't always reflecting his real interests. In this paper, we propose a new approach for enriching the user profile in an evolutionary environment such as a social network. The enrichment takes into account: i) the social behaviour and more precisely the tagging behaviour (that reflects user's interests) and ii) the temporal information (that reflects the dynamic evolution of users' interests). Our approach focus on the concept of temperature that reflects the importance of a resource in each period of time. This concept is used to infer common interests of users tagging the same "important" resource. The originality of our approach relies on combining information tags, users and resources in a way that guarantees a better enrichment for the social user profile. Our approach has been tested and evaluated with the Delicious social database and shows interesting precision values.

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“…Recently, some researchers suggested using users' social information in building users' profiles; such as social connections with other users or groups and pages, and also social behaviours like shares, clicks, and likes or (thumps ups) between users (Fabian Abel, Gao, Houben, & Tao, 2011b;Barla, 2011;Chen, Nairn, Nelson, Bernstein, & Chi, 2010;Hannon, Bennett, & Smyth, 2010;Hannon, Mccarthy, O'mahony, & Smyth, 2012;Hung, Huang, Hsu, & Wu, 2008;Kim, Ha, Lee, Jo, & ElSaddik, 2011;Lu, Lam, & Zhang, 2012;Tao, Abel, Gao, & Houben, 2012). Social information is believed to be useful in enhancing many predictive results of different applications (Ma, Zhou, Liu, Lyu, & King, 2011;Mezghani, Zayani, Amous, & Gargouri, 2012;Yang, Steck, & Liu, 2012;L. Yu, Pan, & Li, 2011).…”

Section: User Profiling In Social Mediamentioning

confidence: 99%

“…Explicit data is given directly by the user; such as demographic information, comments, search queries, and ratings (Mezghani, et al, 2012). Some researchers use users' comments and posts directly to extract keywords to represent users' interests (Hannon, et al, 2010;Lu, et al, 2012), while others directly use the rated items as indication of users' interest (Ma, et al, 2011).…”

Section: Explicit Datamentioning

confidence: 99%

A Survey of User Modelling in Social Media Websites

Abdel-Hafez¹,

Xu²

2013

CIS

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With the widespread of social media websites in the internet, and the huge number of users participating and generating infinite number of contents in these websites, the need for personalisation increases dramatically to become a necessity. One of the major issues in personalisation is building users' profiles, which depend on many elements; such as the used data, the application domain they aim to serve, the representation method and the construction methodology. Recently, this area of research has been a focus for many researchers, and hence, the proposed methods are increasing very quickly. This survey aims to discuss the available user modelling techniques for social media websites, and to highlight the weakness and strength of these methods and to provide a vision for future work in user modelling in social media websites.

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A user profile modelling using social annotations

Cited by 46 publications

References 19 publications

Producing relevant interests from social networks by mining users' tagging behaviour: A first step towards adapting social information

Producing relevant interests from social networks by mining users' tagging behaviour: A first step towards adapting social information

Dynamic enrichment of social users' interests

A Survey of User Modelling in Social Media Websites

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