Using Movie Genres in Neural Network Based Collaborative Filtering Movie Recommendation System to Reduce Cold Start Problem

Abstract

In a recommendation system, a cold start problem occurs when the system is unable to make any inferences for users or items for which it has not yet acquired adequate data. This research proposes a modified version of the Neural Collaborative Filtering model that has revolutionized collaborative filtering recommendation system with its implementation of neural network in the model. The proposed model is called Neural Collaborative Filtering Modified (NCFM). NCFM addresses the cold start problem by reducing the model dependency on users and items interactions with the integration of item feature (movie genres in this case) before the concatenation step in the NCF model. Hit ratio@10 and normalized discounted cumulative gain@10 were used as the evaluation method because they closely represents real-world recommendation scenario. Performance of both models were tested and compared using two movie rating dataset MovieLens1M provided by GroupLens and a modified version of MovieLens1M with significantly less amount of data to simulate the cold start problem called MovieLensCold. The results shows that NCFM can outperforms NCF by more than two times over or around 133% performance increase on MovieLensCold and around 15% increase on MovieLens1M dataset. Keywords— movie recommendation system, cold start problem, collaborative filtering, NCFM, neural network based recommendation system