Social network sites have experienced an explosion in both the number of users and the
amount of user-contributed content in recent years. Today, millions of people visit
Facebook, Twitter and the like to keep up with friends, to engage in random chatter, and
to share and consume photos, news, useful tips and fun stories.
Many active users of social network sites are, however, constantly troubled by
information overload – there are too many other people to interact with and too much
content to read. As a result, the difficulty in finding the right people and content to focus
on has been identified as a key challenge for social network sites.
In this thesis we seek to meet the challenge by designing three personalized recommender
systems. The first system is a people recommender, which helps users find potential
friends to connect with on social network sites; the second system is an information
recommender, which helps users find interesting pieces of information from their online
social network; the third system is a conversation recommender, which helps users find
interesting conversations happening around their online social network.
In designing the recommender systems, we adapt algorithms from related domains and
create new algorithms. We refine these algorithms through offline data analysis. We then
design and improve user interfaces of the recommender systems through participatory design. With the full recommender systems designed and implemented, we deploy these
systems to real users of social network sites, and evaluate a variety of recommender
algorithms online through user studies. Through these user studies we not only provide
qualitative and quantitative comparisons of recommender algorithms, but also provide
broader insights for designing recommender systems on social network sites.