Improving the novelty of retail commodity recommendations using multiarmed bandit and gradient boosting decision tree

Recommender systems are becoming increasingly critical to the success of commerce sales. In spite of their benefits, they suffer from some major challenges including recommendation quality such as the accuracy, diversity, and novelty of recommendations. In the context of retail business, the novelty of recommendations is of especial importance because it can directly affect customers' probabilities of buying commodity and whether to visit stores again. However, tradition algorithms for retail commodity recommendation never consider the problem of improving the novelty of recommendations. To address this, a novel multiarmed bandit and gradient boosting decision tree-based retail commodity recommendation approach is proposed in this article, which is named MGRCR. It can increase recommendations' novelty while maintaining comparable levels of in the context of retailing. The effectiveness of our proposed approach has been proved by comprehensive experiments with real-world commerce datasets and different state-of-the-art recommendation techniques.