User's Social Profile -- Based Web Services Discovery

Kalaï, Ahlem; Zayani, Corinne Amel; Amous, Ikram

doi:10.1109/soca.2015.45

Cited by 9 publications

(15 citation statements)

References 7 publications

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“…of social recommender system to make recommendation of any thing (i.e, friend, product, resource, service, media). The latter is part of our future works which related to Web Service recommendation in order to enhance our previous work [20]. In the next section, we will present some indicators that influence in the trust computation.…”

Section: Trust Measurementmentioning

confidence: 99%

“…Finally, our mechanism returns a trusted egocentric network which represented by a Trust Matrix. The latter can be used as the main input into a social recommender system [21] [15] [36] that we envisage to integrate it in our previous works [20].…”

Section: Social Trust Detection Mechanismmentioning

confidence: 99%

“…For this reason, we propose a dynamic threshold γ that adapts to each user instead of using one static threshold (e.g. [20]) that will be used for all the users. Hence, the best trusted friends of the user-ego will be recommended where the level of social trust LoT (ego, u j ) ≥ γ, and the threshold γ is calculated according to equation 6.…”

Section: Trusted Friends Filteringmentioning

confidence: 99%

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LoTrust: A social Trust Level model based on time-aware social interactions and interests similarity

Kalaï

Abdelghani

Zayani

et al. 2016

2016 14th Annual Conference on Privacy, Security and Trust (PST)

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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 : 18269 Abstract-With the immense growth of online social applications, trust plays a more and more important role in connecting users to each other, sharing their personal information and attracting him to receive recommendations. Therefore, how to obtain trust relationships through mining online social networks became a critical issue. To calculate the level of trust between two users, many computational trust models are proposed which mainly rely on the social network structure, the explicit trust from user to another, the users' behaviors, or the users' similarity, etc. However, the majority of these models ignored the temporal factor. In this paper, we propose a trust relationship detection mechanism from an egocentric social network in order to compute the trust level between an active user and his directed friends. We propose a Level of social Trust model, that we called LoTrust, which is suitable for personalized recommendation purpose. This computational model founded on novel trust metric which is based not only on the users' interests similarity according to their semantic social profiles (RDF/FOAF), but also takes into account the time factor of the users' active interactions (e.g comments, share photo, wall posts, messages). We perform experiments on real life dataset extracted from Facebook. The experimental results demonstrated how our LoTrust model produces satisfactory results than other computational models.

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Section: Trust Measurementmentioning

confidence: 99%

Section: Social Trust Detection Mechanismmentioning

confidence: 99%

Section: Trusted Friends Filteringmentioning

confidence: 99%

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LoTrust: A social Trust Level model based on time-aware social interactions and interests similarity

Kalaï

Abdelghani

Zayani

et al. 2016

2016 14th Annual Conference on Privacy, Security and Trust (PST)

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“…In general, trust is a complex relationship based on a wide range of factors [21] and may be affected by the users' interactions, their interests, etc. However, most of the trust-aware Web service recommendation studies neglect these users' social data and are based only on the measures which are related to the network structure, such as centrality degree [2], similarity of network structure [11] or users' proximity [12,15]. Other research studies [18,19] focused on users' interactions to compute the social trust but all of them have neglected the impact of the time.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we present an enhancement of our previous research on Web service decentralized discovery [11]. Our approach exploits the knowledge of users' social networks to provide higher quality recommendations than current CF approach.…”

Section: Introductionmentioning

confidence: 99%

Expertise and Trust –Aware Social Web Service Recommendation

Kalaï

Zayani

Amous

et al. 2016

Service-Oriented Computing

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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. Abstract. With the increasing number of Web services, the personalized recommendation of Web services has become more and more important. Fortunately, the social network popularity nowadays brings a good alternative for social recommendation to avoid the data sparsity problem that is not treated very well in the collaborative filtering approach. Since the social network provides a big data about the users, the trust concept has become necessary to filter this abundance and to foster the successful interactions between the users. In this paper, we firstly propose a trusted friend detection mechanism in a social network. The dynamic of the users' interactions over time and the similarity of their interests have been considered. Secondly, we propose a Web service social recommendation mechanism which considers the expertise of the trusted friends according to their past invocation histories and the active user's query. The experiments of each mechanism produced satisfactory results.

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