Collaborative filtering recommendation algorithm based on hybrid user model

Collaborative filtering is the most widely used and successful technology for building recommender systems. However it faces challenges of scalability and recommendation accuracy. Collaborative filtering can be divided into memory based and model based. The former is more accurate while the latter performs better in scalability. This paper proposes a hybrid user model. The recommender system based on this model not only holds the advantage of recommendation accuracy in memory-based method, but also has the scalability as good as model-based method. The user model is constructed based on item combination feature and demographic information, and it focuses on searching for set of neighboring users shared with same interest, which helps to improve system scalability. To enhance recommendation accuracy, each feature in user model is given a different weight when computing the similarity between users. Genetic algorithm is adopted to learn the weight values of features. A comparison experiment was performed on MovieLens data set, and the result shows methodology proposed in this paper performs better than conventional collaborative filtering in recommendation accuracy and scalability.