Biblio

In the last few years, cluster ensembles have emerged as powerful techniques that integrate multiple clustering methods into recommender systems. Such integration leads to improving the performance, quality and the accuracy of the generated recommendations. This paper proposes a novel recommender system based on a cluster ensemble technique for big data. The proposed system incorporates the collaborative filtering recommendation technique and the cluster ensemble to improve the system performance. Besides, it integrates the Expectation-Maximization method and the HyperGraph Partitioning Algorithm to generate new recommendations and enhance the overall accuracy. We use two real-world datasets to evaluate our system: TED Talks and MovieLens. The experimental results show that the proposed system outperforms the traditional methods that utilize single clustering techniques in terms of recommendation quality and predictive accuracy. Most importantly, the results indicate that the proposed system provides the highest precision, recall, accuracy, F1, and the lowest Root Mean Square Error regardless of the used similarity strategy.

Recent advances in information filtering have resulted in effective recommender systems that are able to provide online personalized recommendations to millions of users from all over the world. However, most of these systems ignore the explanation purpose while producing recommendations with high-quality results. Moreover, the classification of reviews given to users as explanations is not fully exploited in previous studies. In this paper, we develop a convolutional neural network-based reviews classification method for explainable recommendation systems. The convolutional neural network is used to extract the reviews features for predicting whether the reviews provided as explanations are positive or negative. Based on such additional information, users can understand not only why certain items are recommended for them but also get support to know the nature of such explanations. We conduct experiments on a dataset from Amazon. The experimental results show that our method outperforms state-of-the-art methods.