Inferring semantic interest profiles from Twitter followees

Abstract

Social media based recommendation systems infer users' interests from their social network activity in order to provide personalised recommendations. Typically, the user profiles are generated by analysing the users' posts or tweets. However, there might be a significant difference between what a user produces and what she consumes. We propose an approach for inferring user interests from followees (the accounts the user follows) rather than tweets. This is done by extracting named entities from a user's followees using the English Wikipedia as knowledge base and regarding them as interests. Afterwards, a spreading activation algorithm is performed on a Wikipedia category taxonomy to aggregate the various interests to a more abstract interest profile. With over 7 out of 10 items being relevant to the users in our evaluation, we show that this approach can compete with the state of the art and performs even better in predicting the users' interests than their human friends do.