Recommender systems assist users to make decisions among a huge volume of options. Accuracy-oriented recommender systems focus on the prediction power of algorithms and neglect that users may appreciate diverse and novel recommendations in real-world scenarios. Thus, this paper proposed a multicriteria recommendation model that can optimize the recommendation accuracy, diversity, novelty, and individual tendency simultaneously. Additionally, a new multiobjective bacterial foraging optimization method is proposed to improve its searching capability and the performance of recommendation model. The proposed optimization-based multicriteria recommendation algorithm is compared with existing methods on both benchmark functions and real-world data sets. The results demonstrate that the proposed algorithm is superior to other recommendation algorithms in most cases. This study provides insights in recommendation system design and draws scholarly attention to the optimization-based recommendation strategy.

A class-specific random forest (RF) model with cross-correlation constraints is developed for the spectral-spatial hyperspectral image (HSI) classification. The novelties of this letter are as follows: 1) normalization of the spectral feature vector by using cross correlation in the stochastic process and proposal of a spectral-spatial hybrid feature extraction based on the cross-correlation analysis; 2) establishment of an RF classifier model by using class-specific trees (CSTs); and 3) exploration of the performance of the proposed method by comparing with its several traditional classification methods on two real HSI data sets. Further research on the effects of parameter setup, such as the number of CSTs, spectral constraint scale, and size of the spatial neighbor, is discussed in terms of classification accuracy. Experimental results show that the performance of the proposed method is better than that of the traditional methods.

This letter explores a spectral-spatial tensor-based dimensionality reduction (DR) method to cope with hyperspectral image (HSI) feature extraction and classification. This method uses the Gabor filter banks as the bias spectral-spatial feature hybrider and further integrates the tensor-based alignment strategy for the discriminant locality with sparse factorization by extracting optimal spectral-spatial features and simultaneously maintaining structural relevance. Comparative experimental results with two real HSIs demonstrate that the proposed DR method has a considerable advantage over other traditional feature extraction methods.

This paper presents a new feature extraction method for iris recognition. Since two dimensional complex wavelet transform (2D-CWT) does not only keep wavelet transform’s properties of multiresolution decomposition analysis and perfect reconstruction, but also adds its new merits: approximate shift invariance, good directional selectivity for 2-D image, and limited redundancy, which are useful for iris feature extraction. So, a set of high frequency 2D-CWT coefficients are selected as features for iris recognition. The phase information of the coefficients is used for feature encoding and Hamming distance is adopted for classification. Experimental results show that the proposed algorithm can get good recognition rate.