A Naive Statistics Method for Electronic Program Guide Recommendation System

摘要

In this paper, we propose a naive statistics method for constructing a personalized recommendation system for the Electronic Program Guide (EPG). The idea is based on a primitive approach of N-gram to acquire nouns and compound nouns as prediction features, and then to combine the tf·idf weighting to predict user favorite programs. Our approach unified feedback process, system can incrementally update the vector of extracted features and their scores. It was proved that our system has good accuracy and dynamically adaptive capability.