Combining recommendation systems with a dynamic weighted technique

Recommender systems represent user preferences for items that the user might be interested to view or purchase. These systems have become extremely common in electronic commerce, providing relevant suggestions and directing users towards those items that best meet their needs and preferences. Different techniques have been analysed including content-based, collaborative and hybrid approaches. The last one is used to improve performance prediction combining different recommender systems using the best features of each method, smoothing problems as cold-start. We evaluate our ensemble method using MovieLens dataset with promising results.