Guided-Gated Recurrent Unit for Deep Learning-Based Recommendation System
Igi Ardiyanto · 2019
Discovering and drawing out the relationship between users and items in a service-based companies or organizations are the essence of a recommendation system. It attracts many researches trying to solve such problems. Here we address a novel approach for the recommendation system, incorporating the means of collaborative aspect between the users internal hidden patterns and the items or goods to be recommended. Unlike the existing methods, our algorithm introduces a guiding factor between the user hidden state and the choice over the item set, such that it gives additional degree of freedom for the recommendation system to opt on which factor is more prominent. Experimental results suggest the advantages of the proposed algorithm over the existing state-of-the-art algorithms for the recommendation system.