[Objective] This study proposes an improved paper recommendation framework based on Ensemble Learning and Factorization Machine. It addresses the issues of the existing methods, such as difficulties in processing sparse data and representing features. [Methods] First, we used Convolutional Neural Network, Network Embedding, and other algorithms to obtain feature representations, which were processed by Factorization Machine learners. Homogeneous weak Factorization Machine learners are then trained based on Ensemble Learning. We integrated these weak learners into a stronger learner through the voting mechanism and generated the final recommendations. [Results] We examined the new model with the CiteULike dataset, and the Precision, Accuracy, and F-Measure reached 72.6%, 69.7%, and 76.2%, respectively, 20%, 15%, and 9% higher than the benchmark algorithms. [Limitations] The input, sampling strategy, and processing mode need to be further explored. [Conclusions] The proposed Ensemble Factorization Machine enables effective representation and utilization of sparse data features, enhancing the recommendation performance.
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