Recently, product images have gained increasing attention in clothing recommendation since the visual appearance of clothing products has a significant impact on consumers» decision. Most existing methods rely on conventional features to represent an image, such as the visual features extracted by convolutional neural networks (CNN features) and the scale-invariant feature transform algorithm (SIFT features), color histograms, and so on. Nevertheless, one important type of features, the aesthetic features, is seldom considered. It plays a vital role in clothing recommendation since a users» decision depends largely on whether the clothing is in line with her aesthetics, however the conventional image features cannot portray this directly. To bridge this gap, we propose to introduce the aesthetic information, which is highly relevant with user preference, into clothing recommender systems. To achieve this, we first present the aesthetic features extracted by a pre-trained neural network, which is a brain-inspired deep structure trained for the aesthetic assessment task. Considering that the aesthetic preference varies significantly from user to user and by time, we then propose a new tensor factorization model to incorporate the aesthetic features in a personalized manner. We conduct extensive experiments on real-world datasets, which demonstrate that our approach can capture the aesthetic preference of users and significantly outperform several state-of-the-art recommendation methods.

Skyline, aiming at finding a Pareto optimal subset of points in a multi-dimensional dataset, has gained great interest due to its extensive use for multi-criteria analysis and decision making. Skyline consists of all points that are not dominated by, or not worse than other points. It is a candidate set of optimal solution, which depends on a specific evaluation criterion for optimum. However, conventional skyline queries, which return individual points, are inadequate in group querying case since optimal combinations are required. To address this gap, we study the skyline computation in group case and propose fast methods to find the group-based skyline (G-skyline), which contains Pareto optimal groups. For computing the front k skyline layers, we lay out an efficient approach that does the search concurrently on each dimension and investigates each point in subspace. After that, we present a novel structure to construct the G-skyline with a queue of combinations of the first-layer points. Experimental results show that our algorithms are several orders of magnitude faster than the previous work.

A hybrid navigation approach for mobile robots is presented in this paper, which integrates partial motion planning with emotion-based behavior coordination. According to the local sensing information and assigned goal, the partial motion planning determines a local target point for the mobile robot, and the trajectory composed of the local targets is near optimal in the sense of a given cost function. Behavior coordination based on emotional mechanism is proposed in the navigation control of mobile robots. The emotion system can properly reflect environment states, and then effectively synthesize rational and complex behaviors. The proposed hybrid approach is competent for the mobile robot to navigate autonomously and effectively in unknown environments. Simulation results demonstrate that the robot motion trajectory using the proposed hybrid navigation approach is more reasonable than the one using a pure behavior-based control or emotion-based behavior coordination alone

A novel tracking control scheme for nonholonomic mobile robots is developed, which integrates two adaptive neurons or non-adaptive neurons, backstepping technique and sliding mode. The proposed control system includes a velocity controller embedded with two biological neurons and a torque controller based on sliding mode. An adaptive neuron model is presented to extend the functions of the neuron model. Two adaptive neurons are embedded into the backstepping-based velocity controller to improve the tracking performance of the proposed control scheme. The proposed systems can completely eliminate the sharp speed jumps existing commonly in the mobile robot systems and is able to ensure the mobile robot to navigate safely under any circumstance. The simulation results demonstrate the effectiveness and efficiency of the proposed control scheme.