Leverage Item Popularity and Recommendation Quality via Cost-Sensitive Factorization Machines

The accuracy of recommendation trends to be worse towards the long tail of the popularity distribution of items, but items in the long tail are generally considered to be valuable as they occupy a majority part of entire data. In this paper, we develop an instance-level cost-sensitive Factorization Machine (FM) to tackle the problem. The new algorithm allows the FM model to automatically leverage the trade-off between item popularity and recommendation quality. Specifically, by adding a cost criterion to the loss function, the FM model is now able to discriminate the relative importance of popularity from massive data. In addition, we convert several well-known functions into the popularity weighting functions, thereby demonstrating that the proposed method can fit the model parameters to various kinds of measurements. In the experiments, we assess the performance on a real-world music dataset which is collected from an online music streaming service, KKBOX. The dataset contains 1,800,000 listening records that cover 5,000 users and 30,000 songs. The results show that, the proposed method not only keeps the performance as primitive model but also avoids retrieving too much popular music in the top recommendations.