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- W3133695499 abstract "In this paper, we present an analysis of the user recommendation algorithm of the popular social network Instagram. First, we introduce a framework for the analysis and evaluation of user recommendations in any social network. Then, we build a neighborhood graph for selected active users on Instagram, containing more than 609,000 users and 747,000 relationships. For these users, we also create a dataset of not publicly available personalized recommendation lists. Finally, we apply several graph-based algorithms to this graph to identify Instagram's user recommendation system. We show that an algorithm based on Twitter's Who-to-Follow algorithm is most similar to Instagram's unknown algorithm when recommending users connected to the followees of the target users. Moreover, this algorithm is very cost-effective due to requiring only a relatively small, bipartite graph, compared to the other candidates. Furthermore, we give an analysis of the composition of the user suggestions based on the different reasons for the suggestions." @default.
- W3133695499 created "2021-03-15" @default.
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- W3133695499 date "2021-01-01" @default.
- W3133695499 modified "2023-09-27" @default.
- W3133695499 title "Evaluation of Graph-based Algorithms for Guessing User Recommendations of the Social Network Instagram" @default.
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- W3133695499 doi "https://doi.org/10.1109/icsc50631.2021.00075" @default.
- W3133695499 hasPublicationYear "2021" @default.
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