Abstract: For many online E-commerce companies, it is very important to predict the possibility of consumers purchasing goods. Because the interaction between users and commodities is usually high-dimensional and sparse, the deep factor decomposer algorithm (DeepFM) combines the factor decomposer algorithm (FM) with the deep neural network (DNN), uses FM to deal with low-order feature combination, uses DNN to deal with high-order feature combination, and combines the two methods in parallel, which solves the problem of high-dimensional sparse. However, it ignores the order of purchase, that is, time context information. Aiming at this defect, this paper proposes a time context recommendation system (DeepAFM) which integrates attention and DeepFM, which makes better use of the time context information of user and commodity interaction. Compared with DeepFM model without time context information, the AUC is increased by 1.84%. The results of the comparison and verification show that DeepAFM model has better performance.

Key words: recommendation system, temporal context, attention mechanism, DeepFM