关键词: 智能推荐, 深度学习, 因子分解机, 交叉网络

Abstract:

Most traditional recommendation algorithms only consider user-commodity rating information for recommendation, so that the accuracy of these recommendation models are not high, which ignore the information of user attributes and commodity attributes. Therefore, a hybrid recommendation model based on deep learning（Deep and Cross Factorization Machine, DCFM） is proposed by using factorization machine to mine the relationship between users and commodities in the case of sparse data, cross-network to mine linear combination relation between the first input layer and every high order features of attributes and deep neural network to mine high-order non-linear association. The three parts are connected in parallel, sharing the input layer, and the weighted results are combined by linear stitching as the overall output of the model. Through the simulation experiment on MovieLens movie data set, and compared with Factorization Machine（FM）, Deep Factorization Machine（DeepFM）, Deep Cross Network（DCN） model, the results show that the model has been improved in accuracy, F1-Score and AUC values.

Key words: intelligent recommendation, deep learning, factor machine, cross network