A Method on Long Tail Recommendation Based on Three-Factor Probabilistic Graphical Model

In the Internet era, the explosive rise of information and unprecedented data size has greatly exceeded the receivers’ receiving and handling capability. Accordingly it has become a necessity to capture important and useful information from the massive and complex data. The problem of information overload calls for a more efficient information filtering system and this explains the birth and rise of the recommendation system. In the real recommendation situations, the long tail phenomenon is typically represented, whether in the customers’ rating or their frequency of opting for the items. Actually a deep analysis on the long tail phenomenon helps to promote the e-commerce stakeholders’ performance as well as explore the customers’ preferences, which explains the increasing attention paid on the long tail recommendation. To handle the interpretability of long tail recommendation, a three-factor probabilistic graphical model is proposed. To meet the recommendation system and users’ need for interpretable item recommendation in reality, a three-factor probabilistic graphical recommendation method based on customers’ activity level, the items’unpopularity and customer-item preference level are proposed. In this method, the advantage of probabilistic graphical model in interpreting cause-result relationship is utilized and recommendation accuracy and design novelty in the algorithm can be ensured in this study. The results of experiments indicate that with the advantage of interpretability, the three-factor probabilistic graphical recommendation method can ensure prediction accuracy and perform better in providing novel recommendations.