Improving matrix factorization recommendations for examples in cold start

Recommender systems suggest items of interest to users based on their preferences (i.e. previous ratings). If there are no ratings for a certain user or item, it is said that there is a problem of a cold start, which leads to unreliable recommendations. We propose a novel approach for alleviating the cold start problem by imputing missing values into the input matrix. Our approach combines local learning, attribute selection, and value aggregation into a single approach; it was evaluated on three datasets and using four matrix factorization algorithms. The results showed that the imputation of missing values significantly reduces the recommendation error. Two tested methods, denoted with 25-FR-ME-∗ and 10-FR-ME-∗, significantly improved performance of all tested matrix factorization algorithms, without the requirement to use a different recommendation algorithm for the users in the cold start state.