Big Social Data - Predicting Users' Interests from their Social Networking Activities
Alexiei Dingli, Bernhardt Engerer · 2019
The amount of data produced by Social Network users, whether through direct content creation or as a byproduct of their Social Network usage, is ever increasing. This research presents an approach to predicting unknown user interest in entities based on Entity Extraction from User Generated Content and through the use of a Potential Link Prediction algorithm for recommendations. An algorithm was developed which is able to extract relevant entities from the microtext forming the metadata of Facebook pages liked by a user. These entities are then used in order to suggest other potentially interesting pages to the user. Additionally, crowd-sourced knowledge is used in order to automatically filter out entities which are likely to be irrelevant to future users based on past ratings. Using these filtered entities and by having at least 10 interests disclosed by a user, it is possible to predict further entities of interest to a user, with at least 80% confidence in the predictions.