A collective matrix factorisation approach to social recommendation with eWOM propagation effects Online publication date: Wed, 27-Jun-2018
by Ren-Shiou Liu
International Journal of Big Data Intelligence (IJBDI), Vol. 5, No. 3, 2018
Abstract: In recent years, recommender systems have become an important tool for many online retailers to increase sales. Many of these recommender systems predict users' interests in products by using the browsing history or item rating records of users. However, many studies show that, before making a purchase, people often read online reviews and exchange opinions with friends in their social circles. The resulting electronic word-of-mouth (eWOM) has a huge impact on customer's purchase intention. Nonetheless, most recommender systems in the current literature do not consider eWOM, let alone the effect of its propagation. Therefore, this paper proposes a new recommendation model based on the collective matrix factorisation technique for predicting customer preferences in this paper. A series of experiments using data collected from Epinions and Yelp are conducted. The experimental results show that the proposed model significantly outperforms other closely related models by 5%-13% in terms of RMSE and MAE.
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