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Mining of User Profiles in Online Social Networks for Improved Personalized Recommendations

Time: Thu 2020-12-03 16.00

Location: Sal C, Kistagången 16, Kista, Stockholm (English)

Doctoral student: Shatha Jaradat , Programvaruteknik och datorsystem, SCS

Opponent: Professor James Caverlee, Texas A&M University​

Supervisor: Professor Mihhail Matskin, Programvaruteknik och datorsystem, SCS


We have focused on influencer-based marketing in online social networks as a source of implicit learning about the preferences of social media users. Those users who use social networks on a daily basis are also the online shoppers who are confronted with huge information overload and a wide variety of online products and brands to choose from. The role of digital influencers in promoting products and spreading information to a large scale of followers who engage with the influencers’ posts and interact with them is our key to better understanding of these followers’ tastes and future purchase intentions. Hence, the analysis and the extraction of fine-grained details (which we refer to as user profiling) from digital influencers media content serves in collecting more information about the implicit preferences of their followers. With this knowledge, the chances of offering social media users better personalized services are enhanced. In this thesis, we empower cross-domain recommendations through the development of novel methods and algorithms for improving personalization through the effective mining of user profiles in online social networks. We developed a semantic information extraction framework from social media textual content that is able to capture fine-grained attributes with respect to the defined online shops taxonomy. Results form the aforementioned framework have been applied as input to the approaches we proposed to incorporate extracted textual hints in supporting the visual fine-grained classification of social media images in a dynamic way. Our methods have improved the classification accuracy when compared to state-of-the-art approaches. Moreover, we suggested solutions for incorporating the extracted products’ meta-data in embedding-based personalized recommendation architectures where our strategies improved the recommendations’ quality. In order to speed up the process of preparing large scale social media images datasets for deep learning image analysis, we developed a complete framework for detailed annotation, object localization and semantic segmentation. As our focus is also directed towards the analysis of interactions between social media users, we proposed a neural reinforcement learning approach that is based on estimating the established trust levels between social media users for controlling the amount of recommended updates they get from each other. Moreover, we proposed enhanced topic modelling algorithm for supporting interpretable yet dynamic summarizations of large social media contents.