Context-aware personalized program guide based on neural network

When number of available items surpasses the users´ ability to browse them in a reasonable time, personalized recommender systems are used to assist the users to find the items that would match their interests. In this paper, the design of a context-aware recommender system for digital TV is described. Available programs are represented by their features in the adjacent vector space and genre transform is applied to reduce its dimension. Single-hidden layer feedforward neural network is used as a classifier tool which estimates whether certain program is of substantial interest to the observed user; this network is fed with the data on both program genre and the temporal context related to the user´s watching habits. Special attention is paid to choosing an efficient network training algorithm and unobtrusive user feedback scheme. Good performances of the proposed system are verified through a series of experiments. It is shown that context information speeds the learning process up and reduces the so-called cold start phase without compromising the accuracy of the delivered recommendations.