Item-based collaborative filtering learns user preferences from the user's historical interaction items and recommends similar new items based on the user's preferences. The existing collaborative filtering methods assume that a set of historical items that user has interacted with have the same impact on user, and all historical interaction items are considered to have the same contribution to the prediction of target item, which limits the accuracy of these recommendation methods. In order to solve the problems, a new collaborative filtering recommendation algorithm based on dual most relevant attention network was proposed, which contained two attention network layers. Firstly, the item-level attention network was used to assign different weights to different historical items in order to capture the most relevant items in the user historical interaction items. Then, the item-interaction-level attention network was used to perceive the correlation degrees of the interactions between the different historical items and the target item. Finally, the fine-grained preferences of users on the historical interaction items and the target item were simultaneously captured through the two attention network layers, so as to make the better recommendations for the next step. The experiments were conducted on two real datasets of MovieLens and Pinterest. Experimental results show that, the proposed algorithm improves the recommendation hit rate by 2.3 percentage points and 1.5 percentage points respectively compared with the benchmark model Deep Item-based Collaborative Filtering (DeepICF) algorithm, which verifies the effectiveness of the proposed algorithm on making personalized recommendations for users.

To keep the trade-off of time complexity and accuracy of community detection in complex networks, Community Detection Algorithm based on Clustering Granulation (CGCDA) was proposed in this paper. The granules were regarded as communities so that the granulation for a network was actually the community partition of a network. Firstly, each node in the network was regarded as an original granule, then the granule set was obtained by the initial granulation operation. Secondly, granules in this set which satisfied granulation coefficient were merged by clustering granulation operation. The process was finished until granulation coefficient was not satisfied in the granule set. Finally, overlapping nodes among some granules were regard as isolated points, and they were merged into corresponding granules based on neighbor nodes voting algorithm to realize the community partition of complex network. Newman Fast Algorithm (NFA), Label Propagation Algorithm (LPA), CGCDA were realized on four benchmark datasets. The experimental results show that CGCDA can achieve modularity 7.6% higher than LPA and time 96% less than NFA averagely. CGCDA has lower time complexity and higher modularity. The balance between time complexity and accuracy of community detection is achieved. Compared with NFA and LPA, the whole performance of CGCDA is better.

In order to improve the blind equalization performance of the weighted multimode algorithm, an improved weighted multi-modulus blind equalization algorithm based on a non-linear function of weighting factor was proposed in this paper. The new algorithm used a nonlinear relationship between the mean square error and the weighting factor to improve the speed of convergence and improve the adaptive capacity to different signal-to-noise ratio. In the converging process of the algorithm, with the mean square error decreased, the value of the weighting factor increased gradually. It adjusted the modulus value of the algorithm dynamically, and made the error model match with the signal constellation accurately to reduce the steady-state mean square error. The theoretical analysis and simulation results show that the proposed algorithm reduces the steady-state mean square error, and improves the convergence rate.

In order to further improve the convergence speed of the exponential variable step-size constant module algorithm, a variable step-size constant module algorithm based on the exponential multi-delay error signal autocorrelation was proposed on the basis of the analysis of the error signal autocorrelation, and the multi-delay error signal autocorrelation functions were adopted to control the steps. Compared with the non-delay and unit delay, the multi-delay error signal autocorrelation of the algorithm can provide simpler and more accurate information for training trajectory, so the algorithm converges faster, and the convergence process is smoother and more stable. The underwater acoustic channel simulation experiments further verify the advantage of the algorithm in the convergence speed.