Self-adaptive sampling method based on user preferences

The invention discloses a self-adaptive sampling method based on user preferences. According to the method, triple training data is constructed self-adaptively according to the user preferences reflected in purchase records of a user and the features of a BPR (Bayesian Personalized Ranking) model; and the BPR model is trained. Compared with the conventional training method based on random sampling, the self-adaptive sampling method designed by the invention has the advantages that the training convergence of the conventional BPR model can be accelerated; in a practical training process, an individual model parameter value has little change in each round of model training, and therefore the little change is insufficient to result in great change of the practically observed phenomenon in commodity ranking; a strategy for reducing the construction cost of the triple training embodiment is specially designed; therefore, compared with the conventional random sampling method, the method increases a little part of expense as the cost; the model prediction precision is not reduced; and moreover, the training convergence of the BPR model is greatly accelerated.