Collaborative Personalized Twitter Search with Topic-Language Models

The vast amount of real-time and social content in microblogs results in an information overload for users when searching microblog data. Given the user's search query, delivering content that is relevant to her interests is a challenging problem. Traditional methods for personalized Web search are insufficient in the microblog domain, because of the diversity of topics, sparseness of user data and the highly social nature. In particular, social interactions between users need to be considered, in order to accurately model user's interests, alleviate data sparseness and tackle the cold-start problem. In this paper, we therefore propose a novel framework for Collaborative Personalized Twitter Search. At its core, we develop a collaborative user model, which exploits the user's social connections in order to obtain a comprehensive account of her preferences. We then propose a novel user model structure to manage the topical diversity in Twitter and to enable semantic-aware query disambiguation. Our framework integrates a variety of information about the user's preferences in a principled manner. A thorough evaluation is conducted using two personalized Twitter search query logs, demonstrating a superior ranking performance of our framework compared with state-of-the-art baselines.