On the Quality of Semantic Interest Profiles for Online Social Network Consumers

Social media based recommendation systems infer users' interests and preferences 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 and broader interest profile. We evaluate the coverage of followee lists in terms of named entities and show that they provide sufficient input to infer comprehensive semantic interest profiles. Further, we compare the profiles created with the followee-based approach against tweet-based profiles. With over 7 out of 10 items being relevant to the users in our evaluation, we show that the followee-based approach can compete with the state of the art and performs even better in predicting the users' interests than their human friends do.