Hybrid recommender system with review helpfulness features

Nowadays, a recommendation system is an important technique in the development of electronic-commerce systems and the most popular approaches that use in many recommendation systems are a collaborative filtering algorithm. However, it still has problems such as scalability and sparse data. There are several previous methods used to deal with the weakness of collaborative filtering techniques such as a hybrid user model, but the results show their disadvantages in practical use. In this paper, we proposed a hybrid recommender system with review helpfulness features, which we have used to construct the hybrid model. In the experiment, the results of three recommendation techniques are compared: collaborative filtering based on hybrid user model, user-based collaborative filtering and our proposed method. The experimental results show that our proposed method is more efficient than other methods with the same dataset, in terms of accuracy. In addition, our proposed algorithm can decrease time consuming by constructing the hybrid model in the off-line phase and calculates the recommendation results in on-line phase.