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In real recommendation systems, implicit feedback data is more common and easier to obtain, and recommendation algorithms based on such data will be more applicable. However, implicit feedback data cannot directly express user preferences. Meanwhile, data sparsity caused by massive data is still an urgent problem to be solved in recommendation system. In response to this phenomenon, this paper proposes a deep collaborative filtering algorithm. In the perspective of implicit feedback, this method uses the advantages of convolutional neural network for effective learning of the nonlinear interaction of users and items and the characteristics of collaborative filtering algorithm for modeling the linear interaction of users and items and combines the two methods for recommendation. Finally, the baseline method is set up and the comparative experiment and parameter adjustment is carried out. The experimental results show that the proposed algorithm has significantly improved the recommendation accuracy on public dataset (Yahoo! Movie). The parameter adjustment results show that, under the condition of uniformly collecting negative feedback data and setting a certain number of convolution layers, the sparser the data is, the better the recommendation performs. As a result, this paper has made some progress in solving the problem of data sparsity and enriching the research of recommendation system. [ABSTRACT FROM AUTHOR]

It is a critical task to provide recommendation on implicit feedback, and one of the biggest challenges is extreme data sparsity. To tackle the problem, a graph kernel-based link prediction method is proposed in this paper for recommending crowdfunding projects combining graph computing with collaborative filtering. First of all, an investor-project bipartite graph is established based on transaction histories. Then, a random walk graph kernel is constructed and computed, and a one-class SVM classifier is built for link prediction based on implicit feedback. At last, top N recommendations are made according to the ranking of investor-project pairs. Comparative experiments are conducted and the results show that the proposed method achieves the best performance on extremely sparse implicit feedback and outperforms baselines. This paper is of help to improve the success rate of crowdfunding by personalized recommendation and is of significance to enrich the research in recommendation systems. [ABSTRACT FROM AUTHOR]