Demand Prediction Using Machine Learning Methods and Stacked Generalization

Supply and demand are two fundamental concepts of sellers and customers. Predicting demand accurately is critical for organizations in order to be able to make plans. In this paper, we propose a new approach for demand prediction on an e-commerce web site. The proposed model differs from earlier models in several ways. The business model used in the e-commerce web site, for which the model is implemented, includes many sellers that sell the same product at the same time at different prices where the company operates a market place model. The demand prediction for such a model should consider the price of the same product sold by competing sellers along the features of these sellers. In this study we first applied different regression algorithms for specific set of products of one department of a company that is one of the most popular online e-commerce companies in Turkey. Then we used stacked generalization or also known as stacking ensemble learning to predict demand. Finally, all the approaches are evaluated on a real world data set obtained from the e-commerce company. The experimental results show that some of the machine learning methods do produce almost as good results as the stacked generalization method.Comment: Proceedings of the 6th International Conference on Data Science, Technology and Application

Four payment models for the multi-mode resource constrained project scheduling problem with discounted cash flows

In this paper, the multi-mode resource constrained project scheduling problem with discounted cash flows is considered. The objective is the maximization of the net present value of all cash flows. Time value of money is taken into consideration, and cash in- and outflows are associated with activities and/or events. The resources can be of renewable, nonrenewable, and doubly constrained resource types. Four payment models are considered: Lump sum payment at the terminal event, payments at prespecified event nodes, payments at prespecified time points and progress payments. For finding solutions to problems proposed, a genetic algorithm (GA) approach is employed, which uses a special crossover operator that can exploit the multi-component nature of the problem. The models are investigated at the hand of an example problem. Sensitivity analyses are performed over the mark up and the discount rate. A set of 93 problems from literature are solved under the four different payment models and resource type combinations with the GA approach employed resulting in satisfactory computation times. The GA approach is compared with a domain specific heuristic for the lump sum payment case with renewable resources and is shown to outperform it

International competitiveness power and human development of countries

Human development should be the ultimate objective of human activity and its aim should be healthier, longer, and fuller lives. It is expected that if the competitiveness of a country is suitably managed, human welfare will be enhanced as a consequence. The research described here seeks to explore the relationship between the competitiveness of a country and its use for human development. For this purpose, 45 countries were evaluated using data envelopment analysis, where the global competitiveness indicators are taken as input variables and the human development index indicators as output variables. A detailed analysis is also conducted for the emerging economies

Kaynak kısıtlı proje çizelgelemede indirgenmiş nakit akışı maksimizasyonu için bir genetik algoritma yaklaşımı

Bu çalısmada kaynak kısıtlı proje çizelgelemede indirgenmis nakit akısını ençoklamak için gelistirilen bir genetik algoritma sunulmaktadır. Problem hem yenilenebilir hem de yenilenemez kaynaklar göz önüne alınarak tanımlanmaktadır. Kaynakların uygulanmasında sonlu sayıda mod söz konusudur. Genetik algoritmada, çok-bilesenli, düzgün, sıralama temelli bir çaprazlama operatörü kullanılmıstır. Bu çaprazlama operatörünün öncüllük kısıtlarını ihlal etmeyisi önemli bir avantaj sağlamaktadır. Genetik algoritmanın parametrelerinin saptanması için bir meta-seviye genetik algoritma uygulanmıstır. Önerilen algoritmanın sınanması için teknik yazında mevcut 93 problemlik bir test problem kümesi kullanılmıstır. Ayrıca, salt yenilenebilir kaynaklar problemi için, özel amaçlı bir algoritma ile karsılastırma yapılmıs ve önerilen algoritmanın özellikle büyük boyutlu problemlerde basarılı olduğu gösterilmistir

A decision support system to evaluate the competitiveness of nations

The measurement of competitiveness and strategy development is an important issue for policy makers. The aim of this paper is to explore methodological transparency as a viable solution to problems created by existing aggregated indices as well as to conduct a detailed analysis on the ongoing performance of nations’ competitiveness. For this purpose, a methodology composed of three steps is used. To start, a combined clustering analysis methodology is used to assign countries to appropriate clusters. In current methods, country clustering is generally based on GDP. However, we suggest that GDP alone is insufficient to define the stage of competitiveness a country belongs. In the proposed methodology, 135 criteria are used for a proper classification of the countries. Relationships between the criteria and classification of the countries are determined using Artificial Neural Networks (ANNs). ANN provides an objective method for determining the attribute/criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, in the third step, the countries of interest are ranked based on weights generated in the previous step. Beyond the ranking of countries, the proposed methodology can also be used to identify those attributes that a given country should focus on in order to improve its position relative to other countries, i.e., to transition from its current cluster to the next higher one. As a final analysis, the dynamic change of the rank of the countries over years has also been investigated

How to improve the innovation level of a country? A Bayesian net approach

This study aims to provide strategic guidelines to policy makers who are developing strategies to improve their country’s innovativeness. In this paper, we claim that innovation cannot be related only to some factors inherent in the environment of a country, nor is it a single entity to be managed without any linkages to the rest of the actors comprising the competitiveness of a country. Hence, a comprehensive study on innovation should cover the interaction between competitiveness indicators and innovation. For this purpose, the innovation performance of 148 countries is analyzed using an integrated cluster analysis and a Bayesian network framework. These countries are first clustered based on the average values of their competitiveness indicators representing 12 pillars and several sub-pillars adopted from the Global Competitiveness Reports of World Economic Forum for the 2009-2012 period. As a result, five appropriate clusters emerge: Leaders, Followers, Runners Up, Developing Ones, and Laggers. A factor analysis is then conducted to reveal the main characteristics of each cluster in terms of competitiveness indicators. Subsequently, a Bayesian network is constructed and sensitivity analyses are performed to reveal important policies for each cluster

The basic competitiveness factors shaping the innovation performance of countries

The goal of this research is to use Bayesian Networks to discover the relations among the components of competitiveness and the innovation level of countries.For this purpose, initially the competitiveness performance of 148 countries is analyzed using an integrated cluster analysis and factor analysis framework. This facilitates the basic areas where each cluster group demonstrates a good performance and those where they need improvements relative to the othergroups in order to increase their competition level. Subsequently; a Bayesian Network is constructed using WinMine software based on competitiveness indicators drawn from WEF pillars and sub-pillars. This analysis, in its turn,investigates whether the competitiveness stage to which a country belongs has an important impact on its innovation performance and highlights, which of the basic competitiveness variables has a significant impact in shaping its innovation level

Enriching User Shopping History: Empowering E-commerce with a Hierarchical Recommendation System

Recommendation systems can provide accurate recommendations by analyzing user shopping history. A richer user history results in more accurate recommendations. However, in real applications, users prefer e-commerce platforms where the item they seek is at the lowest price. In other words, most users shop from multiple e-commerce platforms simultaneously; different parts of the user's shopping history are shared between different e-commerce platforms. Consequently, we assume in this study that any e-commerce platform has a complete record of the user's history but can only access some parts of it. If a recommendation system is able to predict the missing parts first and enrich the user's shopping history properly, it will be possible to recommend the next item more accurately. Our recommendation system leverages user shopping history to improve prediction accuracy. The proposed approach shows significant improvements in both NDCG@10 and HR@10