User Profile Matching in Social Networks

Abstract

Abstract-Inter-social networks operations and functionalities are required in several scenarios (data integration, data enrichment, information retrieval, etc.). To achieve this, matching user profiles is required. Current methods are so restrictive and do not consider all the related problems. Particularly, they assume that two profiles describe the same physical person only if the values of their Inverse Functional Property or IFP (e.g. the email address, homepage, etc.) are the same. However, the observed trend in social networks is not fully compatible with this assumption since users tend to create more than one social network account (for personal use, for work, etc.) while using same or different email addresses. In this work, we address the problem of matching user profiles in its globality by providing a suitable matching framework able to consider all the profile's attributes. Our framework allows users to give more importance to some attributes and assign each attribute a different similarity measure. The set of experiments conducted with our default/recommended attribute/similarity measures shows the superiority of our proposal in comparison with current ones.

Social network analysis to influence career development

“…We propose a reciprocal-weighted euclidean similarity function (RWD), which is inspired by the hybrid weighted euclidean function and existing research works on matching user profiles of a network (Carbonell et al 2014;Raad et al 2010), to consider self-assigned weights and position orders to profile attributes, when determining similarity measure across profiles. Let L and U be two learner profiles represented by n-dimensional attribute vector, L = (l 1 , l 2 , … , l n ) and U = (u 1 , u 2 , … , u n ) depicting n pre-established measurements made associated with the learner from n attributes, respectively A 1 , A 2 , ⋯ A n which represent common social interests.…”

Prediction of purchase behaviors across heterogeneous social networks

“…Raad [21] used most of the profile attributes, like username, nickname, mailbox, image, etc., and gave different importance to the attributes. Lofciu [22] combined user tags in user profiles to measure the distance between user profiles for identification.…”

Privacy in Online Social Networks

“…Many of these available services are designed to help foster information sharing [55], bridge online and offline connections to enforce interactions [56], provide instant information help [46], and enable users to derive a variety of uses and gratifications from these sites [39]. To make use of the provided functionalities and to stay tuned with their related members, users create several accounts on various social networks where they disclose personal information with varying degrees of sensitivity [57]. Personal information available on these networks commonly describes users and their interactions, along with their published data.…”

“…Social network users create several accounts on various sites where they disclose personal and professional information [57]. Link-based node identification can be used to associate a user profile to a real-world entity (person).…”