USBE: User-similarity based estimator for multimedia cold-start recommendation

To address user cold-start challenge in multimedia recommender systems, we proposed a new model named USBE in this paper. The model doesn’t take the new user’s personal and social information as the necessary parameters to solve cold-start challenge, and new user can complete cold-start by having a simple system experience. Based on the user-similarity and the discrimination of the multimedia items, the model can recommend suitable items for cold-start users and let users choose and give feedback independently. Our model is lightweight and low delay, and provides a new cold-start mode. To complement USBE model, we proposed a cyclic training multilayer perceptron model (Re-NN) to get the strategy of new user’s user-similarity changes. Experiments on a real-world movie recommendation dataset Movielens show: Our model has good results and achieves state-of-the-art after 4 rounds of cold-start recommendations.