Ranking as an important task in information systems has many applications, such as document/webpage retrieval, collaborative filtering and advertising. The last decade has witnessed a growing interest in the study of learning to rank as a means to leverage training information in a system. In this paper, we propose a new learning to rank method, i.e. BLM-Rank, which uses a linear function to score samples and models the pairwise preference of samples relying on their scores under a Bayesian framework. A stochastic gradient approach is adopted to maximize the posterior probability in BLM-Rank. For industrial practice, we have also implemented the proposed algorithm on Graphic Processing Unit (GPU). Experimental results on LETOR have demonstrated that the proposed BLM-Rank method outperforms the state-of-the-art methods, including RankSVM-Struct, RankBoost, AdaRank-NDCG, AdaRank-MAP and ListNet. Moreover, the results have shown that the GPU implementation of the BLM-Rank method is ten-to-eleven times faster than its CPU counterpart in the training phase, and one-to-four times faster in the testing phase.

In this paper, a block implementation of high-speed IIR adaptive noise canceller is proposed. First, the block difference equation of an IIR filter is derived by the difference equation for high-speed signal processing. It is shown that the computational complexity for updating the coefficients of IIR adaptive filter can be reduced by using the relations between the elements of coefficient matrices of block difference equation. Secondly, the block implementation of IIR adaptive noise canceller is proposed in which the convergence rate is increased by successively adjusting filter Q-factors. Finally, the usefulness of proposed block implementation is verified by the computer simulations.