Abstract:  The traditional collaborative filtering algorithm uses the user-item scoring matrix as the input of data to try to find the most similar users or projects. This method ignores the intrinsic link between the user and the project. Aiming at the above problems, this paper proposes a model construction of multi-layer data, which finds multi-dimensional sequences between different levels, mines frequent multi-dimensional sequence patterns, and outputs association rules. The score matrix is improved by the output association rule. The improved data contains the relationship between the user and the project, and the TOP-N recommendation item is output through the collaborative filtering algorithm. The experimental results on MovieLens dataset show that the proposed method can optimize the recommended performance of the model.

Key words: association rules, multi-layer, multi-dimensional, recommendation system