Utilizing popularity characteristics for product recommendation
This paper presents a novel approach to automated product recommendation based on the popularity characteristics of products. Popularity plays a significant role in the consumer purchasing process but has not been given much attention in recommendation research. A three-dimensional model of popularity is used to develop popularity classes of products. These are joined with the MovieLens dataset to create a hybrid movie recommendation system that combines genre and popularity information. As compared with collaborative filtering, the hybrid system shows positive results under the conditions of data sparsity and cold-starting. Many interesting issues for further research are suggested.
automated product recommendation sparsity cold-starting hybrid recommender system naive bayesian popularity-based recommendation popularity model