Integrating Spectral-CF and FP-Growth for Recommendation

In the era of information overload, both information consumers and information producers have encountered great challenges: for information consumers, it is very difficult to find information of interest from a large amount of information. The recommendation system is an important tool to resolve this contradiction. Despite the popularity of Collaborative Filtering (CF), CF-based methods are haunted by the cold-start and data sparseness problems. This paper took commodity recommendation as to the research object and proposed a recommendation algorithm that combines Spectral-CF and FP-Growth. Firstly, Firstly, the association rule algorithm FP-Growth is mine the association rules of the target user and the target item directly, and recommend the collection of items with higher similarity for the user. Secondly, using a spectral collaborative filtering algorithm Perform convolution operations in the spectral domain. Finally, providing the final result by combining the Spectral-CF and FP-Growth recommendation. The experimental results on the MovieLens dataset show that the proposed method can better solve the problem of data sparseness and cold-start problems, improvement the accuracy of recommendation.