A Graph Theoretic Approach for Multi-Objective Budget Constrained Capsule Wardrobe Recommendation

Traditionally, capsule wardrobes are manually designed by expert fashionistas through their creativity and technical prowess. The goal is to curate minimal fashion items that can be assembled into several compatible and versatile outfits. It is usually a cost and time intensive process, and hence lacks scalability. Although there are a few approaches that attempt to automate the process, they tend to ignore the price of items or shopping budget. In this article, we formulate this task as a multi-objective budget constrained capsule wardrobe recommendation ( MOBCCWR ) problem. It is modeled as a bipartite graph having two disjoint vertex sets corresponding to top-wear and bottom-wear items, respectively. An edge represents compatibility between the corresponding item pairs. The objective is to find a 1-neighbor subset of fashion items as a capsule wardrobe that jointly maximize compatibility and versatility scores by considering corresponding user-specified preference weight coefficients and an overall shopping budget as a means of achieving personalization. We study the complexity class of MOBCCWR , show that it is NP-Complete, and propose a greedy algorithm for finding a near-optimal solution in real time. We also analyze the time complexity and approximation bound for our algorithm. Experimental results show the effectiveness of the proposed approach on both real and synthetic datasets.

Global dynamics of a Lotka–Volterra competition patch model*

The global dynamics of the two-species Lotka–Volterra competition patch model with asymmetric dispersal is classified under the assumptions that the competition is weak and the weighted digraph of the connection matrix is strongly connected and cycle-balanced. We show that in the long time, either the competition exclusion holds that one species becomes extinct, or the two species reach a coexistence equilibrium, and the outcome of the competition is determined by the strength of the inter-specific competition and the dispersal rates. Our main techniques in the proofs follow the theory of monotone dynamical systems and a graph-theoretic approach based on the tree-cycle identity.

Evaluating the innovation capability of cluster-based firms: a graph-theoretic approach

Purpose This paper aims to propose an assessment framework to evaluate companies' innovation capability in the context of industrial clusters. Design/methodology/approach The assessment framework was built based on the Graph-Theoretic Approach (GTA) to measure the influence of the factors and sub-factors of innovation capabilities. To quantify the level of interdependence between factors and sub-factors of innovation capability Delphi method was adopted. The authors developed five case studies in firms from an Information and Communications Technology and Creative Economy cluster in Northeastern Brazil to test the framework's applicability. Findings The results showed that identifying and evaluating the factors of innovation capability allows a larger understanding of what affects these capabilities to a greater or lesser extent and contributes to strategic decision-making. Research limitations/implications The framework evaluates the innovation capability of each firm, not providing an index for the whole industrial cluster. Besides, the framework does not consider the innovations developed by the companies through the innovation's capabilities. As the Delphi technique was adopted to analyze the levels of influence or interdependence between factors and sub-factors of innovation capability, different experts may lead to different results. Practical implications Among the managerial implications, the authors can highlight the innovation capability index as a practical performance measure to stimulate improvement initiatives regarding innovations in industrial clusters. Besides, as the proposed framework is generic, research organizations, public institutions and regional governments can adopt it to analyze innovation capabilities in cluster-based companies. Originality/value Previous industrial cluster studies have concentrated on knowledge transfer as the main attribute influencing innovation capabilities. The literature also presents assessment frameworks focusing on qualitative analyses or innovation capabilities outcomes (patents and products). Differently, the authors proposed a quantitative assessment framework considering specific factors (and sub-factors) of innovation capabilities in industrial clusters.

Quantifying geospatial interdependencies of ICT and power system based on open data

In order to evaluate the effectiveness of innovative services and technologies spanning over the ICT-enabled power system, realistic models are required. Although nowadays there is a wide range of power system data models, these models do not include a lifelike representation of the ICT system architecture and do not consider the level of interconnectedness of the two systems. In this paper, we propose a methodology for quantification of the geospatial relations between ICT and power system based on openly available data. We describe a graph-theoretic approach, formulate a problem of assessing geospatial relations and discuss the methods required to process publicly available data, retrieve scenarios for different regions and quantify the level of interdependence between ICT and power system.