Keeping up with the Influencers: Improving User Recommendation in Instagram using Visual Content
Marco Bertini, Andrea Ferracani, Riccardo Papucci, Alberto Del Bimbo · 2020
In the social media domain user-to-user recommendation is an important factor to suggest new content and to strengthen the user social circle. In this paper we investigate how to improve user-to-user recommendation exploiting a user similarity metric computed analysing the photos shared by users on their Instagram profile. We consider in particular users with an established credibility and audience, the so called "influencers". The main idea is that if two influencers publish photos containing similar content it is more likely that they share the same interests and are similar. Moreover, users that follow other users sharing related content are also more similar. Similarity between influencers' photo collections is estimated through neural network embeddings, using a network trained to classify photo collections in categories of interest. An hybrid recommendation approach, which combines collaborative filtering and results from this compact representation of visual content of photo collections, is proposed. Experiments on a large dataset of ~4.8M Instagram users show how our visual approach enhances the performance of a user-to-user recommender with respect to a baseline recommendation algorithm based on collaborative filtering.