Learning to rank, a task applying machine learning techniques to rank the expected information of the users, such as movie items users might be interested in. It is useful for collaborative filtering, which is regarded as a hot subfield of computer supported collaborative work(CSCW). In this paper, we propose an algorithm based on RankBoost to rank expected information of the users more accurately. The main advantage of the algorithm against RankBoost is to add a Weight Matrix regularizer to rank the relevance levels of the information smoothly and locally based on graph methods. The experimental results on the public LETOR datasets show that the proposed algorithm performs better than the baseline algorithm, indicating that the method is promising.

Question Classification plays an important role in most Question Answering systems. In this paper, we exploit semantic features in Support Vector Machines (SVMs) for Question Classification. We propose a semantic tree kernel to incorporate semantic similarity information. A diverse set of semantic features is evaluated. Experimental results show that SVMs with semantic features, especially semantic classes, can significantly outperform the state-of-the-art systems.