An Approach to Collaborative Filtering by ARTMAP Neural Networks

Recommender systems are now widely used in e-commerce applications to assist customers to find relevant products from the many that are frequently available. Collaborative filtering (CF) is a key component of many of these systems, in which recommendations are made to users based on the opinions of similar users in a system. This paper presents a model-based approach to CF by using supervised ARTMAP neural networks (NN). This approach deploys formation of reference vectors, which makes a CF recommendation system able to classify user profile patterns into classes of similar profiles. Empirical results reported show that the proposed approach performs better than similar CF systems based on unsupervised ART2 NN or neighbourhood-based algorithm.

Viable Model of the Enterprise – a Cybernetic Approach for Implementing the Information Technologies in Management

The purpose of the current paper is to present the developed methodology of viable model based enterprise management, which is needed for modern enterprises to survive and growth in the information age century. The approach is based on Beer’s viable system model and uses it as a basis of the information technology implementation and development. The enterprise is viewed as a cybernetic system which functioning is controlled from the same rules as for every living system.