Deep factorization machine model based on attention capsule - Yiran GU; Zhupeng YAO; Haigen YANG (Tongxin xuebao)
Aiming at the problems of single feature combination of recommendation model, resolution of a large amount of valuable feature information, and over-fitting in deep learning, a new attentional scoring mechanism called attention capsule was designed, and a deep factorization machine model based on attention capsule was proposed.Users’ historical clicking and candidate items were processed through weight calculation based on the DeepFM model, reducing the impact of irrelevant features on the model, and the differential impact of different historical behaviors on users’ interests was fully explored.The adaptive regularization formulation was added to the training, which effectively reduced over-fitting without affecting the training speed.The comparison test on two public data sets shows that the proposed model is significantly enhanced in loss function and GAUC compared to other models.