In singular value decomposition++(SVD++), inner product of user and item feature vector is regarded as user's rating of items. However, inner product cannot capture the high-order nonlinear relationship between the user and the item. In addition, the contribution of different interactive items cannot be distinguished when user's implicit feedback is incorporated in SVD++. A recommendation algorithm based on deep neural network and weighted implicit feedback is proposed to solve the two problems. Deep neural network is adopted to model the relationship between the user and the object and attention mechanism is utilized to calculate the weight of historical interactive items in modeling user's implicit feedback. Experiments on public datasets verify the effectiveness of the proposed algorithm.
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2020, Vol. 33 
