Performance Improvement of Recommender Hybrid Techniques Using GRU for Rating Calculation

Abstract

The recommender system has gained attention in the past few years, because it can provide benefits for both the owner of the company and the user to get relevant items quickly. Collaborative filtering is the most commonly used recommender system method because of several advantages. However, collaborative filtering still has several issues such as cold start, sparse data, gray sheep and dynamic taste. Many studies have tried to accomplish these issues with various approaches. In general, the approach used is hybrid technique to cover the weaknesses of each other. One study has tried to accomplish these issues using a 7 block hybrid technique approach. However, the study has problem when dealing with reviews with varying length of sentences (from very short to very long) for calculating ratings, hence it can reduce the overall quality of the recommender system. This paper aims to propose another approach in calculating rating using gated recurrent unit (GRU) and it will be combined with other blocks of hybrid technique. In addition, new blocks will be added, namely demographic filtering. Based on the results obtained, the proposed method provides better results compared to method of previous study.