Influence and performance of user similarity metrics in followee prediction


Followee recommendation is a problem rapidly gaining importance in Twitter as well as in other micro-blogging communities. Hence, understanding how users select whom to follow becomes crucial for designing accurate and personalised recommendation strategies. This work aims at shedding some light on how homophily drives the formation of user relationships by studying the influence of diverse recommendation factors on tie formation. Each recommendation factor was studied considering multiple alternatives for assessing them in terms of user similarity. A data analysis comparing the similarity amongst Twitter users and their followees, regarding two commonly-used followee recommendation factors (topology and content) was performed in the context of a followee recommendation task. This study is amongst the firsts to analyse the effect of the different criteria for followee recommendation in micro-blogging communities, and the importance of thoroughly analysing the different aspects of user relationships to define the concept of user similarity. The study showed how the choice of the different factors and assessment alternatives affects followee recommendation. It also verified the existence of certain patterns regarding friends and random users’ similarities, which can condition the adequacy of the available similarity metrics.

Journal of Information Science