A Hybrid Recommender System using Genetic Algorithm and kNN Approach

Recommender Systems are now popular both commercially and in the research community, where various approaches have been adopted and validated on large scale. In content-based filtering, the systems examine items previously chosen by the actual user, whereas in collaborative filtering, recommendations are based on the information of similar users or items. We also analyzed that recommendations are also influenced by the factors such as age, gender and some other user profile information. In our work both content and collaborative techniques and some demographic information are combined into a hybrid approach, where additional content features are used to improve the accuracy of collaborative filtering. Also we are using the genetic algorithm and k-NN algorithm to provide recommendations to the user. To evaluate precision, recall and F1-measure performance parameters are used.