A New Similarity Measure Based on Mean Measure of Divergence for Collaborative Filtering in Sparse Environment

AbstractMemory based algorithms, often referred to as similarity based Collaborative Filtering (CF) is one of the most popular and successful approaches to provide service recommendations. It provides automated and personalized suggestions to consumers to select variety of products. Typically, the core of similarity based CF which greatly affect the performance of recommendation system is to finding similar users to a target user. Conventional similarity measures like Cosine, Pearson correlation coefficient, Jaccard similarity suffer from accuracy problem under sparse environment. Hence in this paper, we propose a new similarity approach based on Mean Measure of Divergence that takes rating habits of a user into account. The quality of recommendation of proposed approach is analyzed on benchmark datasets: ML 100K, ML-1M and Each Movie for various sparsity levels. The results depict that the proposed similarity measure outperforms existing measures in terms of prediction accuracy

Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation