Human click behavior prediction is crucial for recommendation scenarios such as online commodity or advertisement recommendation, as it is helpful to improve the quality and user satisfaction of services. In recommender systems, the concept of click-through rate (CTR) is used to estimate the probability that a user will click on a recommended candidate. Many methods have been proposed to predict CTR and achieved good results. However, they usually optimize the parameters through a global objective function such as minimizing logloss or root mean square error (RMSE) for all training samples. Obviously, they intend to capture global knowledge of user click behavior but ignore local information. In this work, we propose a novel approach of retrieval-based factorization machines (RFM) for CTR prediction, which can effectively predict CTR by combining global knowledge which is learned from the FM method with the neighbor-based local information. We also leverage the clustering technique to partition the large training set into multiple small regions for efficient retrieval of neighbors. We evaluate our RFM model on three public datasets. The experimental results show that RFM performs better than other models in metrics of RMSE, area under ROC (AUC), and accuracy. Moreover, it is efficient because of the small number of model parameters.
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