Recommender systems have emerged in response to the problem of information overload and can help users to find their interest content. In order to alleviate the sparsity problem of recommender systems and increase the accuracy and diversity of recommendation results, we propose an effective collaborative filtering recommendation method. We adopt PSO-KHM algorithm which composes of Particle Swarm Optimization (PSO) and K-Harmonic means (KHM) to cluster users and then use improved collaborative filtering algorithm to compute the similarity between these clustered users. Further more, we take into account many influential factors in the process of similarity computation. The simulation results on two real-world datasets show that our algorithm achieves superior performance to existing methods.
In this letter, the learning vector quantization (LVQ) from machine learning (ML) is adopted into the large-scale multiple-input multiple-output (MIMO) detection to improve the detection performance. Inspired by the decision region from lattice decoding, the random Gaussian noises are applied in the proposed learning vector quantization-aided detection (LVQD) algorithm for data generation. Then, based on the classification, supervised learning is activated to update the targeted prototype vector iteratively, so as to a better detection performance. Meanwhile, the decoding radius in lattices is also used to serve as a preprocessing for LVQD, which leads to an efficient detection without performance loss. Finally, simulation results confirm that considerable performance gain can be achieved by the proposed LVQD algorithm, which suits well for suboptimal detection schemes.
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