Comparing Collaborative Filtering and Hybrid based Approaches for Movie Recommendation
Noor Ifada, Triyani Fatchur Rahman, Mochammad Kautsar Sophan
Abstract
This paper analyzes and compares the performance of the Collaborative Filtering and Hybrid based approaches in generating movie recommendations. The Collaborative Filtering approach uses the rating data consisting of two main phases: (1) movie similarity and (2) movie rating prediction. Meanwhile, the Hybrid based approach adds the benefit of a Content-based to the Collaborative Filtering based approach. Thus, it uses both the rating and movie data and is consisting of four main phases: (1) text preprocessing, (2) term weighting, (3) movie clustering, and (4) Collaborative Filtering based approach. Empirical results show that the recommendation performances of both approaches are linear to the size of the movie neighbourhood. However, the Hybridbased approach's required neighbourhood size is naturally a lot smaller than that of the Collaborative Filtering since the former employs a clustering technique. The performance comparisons show that the Collaborative Filtering based approach always outperforms the Hybrid based at any top-N position in Precision and NDCG metrics. These findings conjecture that the Hybrid approach does not always improve the Collaborative Filtering approach in movie recommendation.