【摘要】 推荐系统能够根据用户的兴趣特点和购买行为,向用户推荐感兴趣的信息和商品。随着用户生成内容UGC逐渐成为当前Web应用的主流,基于UGC的推荐也得到了广泛关注。区别于传统推荐中用户与物品的二元交互,有的UGC推荐采用协同过滤方法,提出了消费者、物品和生产者的三元交互,进而提高了推荐准确度,但大多算法都集中在推荐的性能而忽略了对鲁棒性的研究。因此,通过结合对抗性学习和协同过滤的思想,提出了一种基于对抗性学习的协同过滤推荐算法。首先在三元关系模型参数上加入对抗性扰动,使模型的性能降至最差,与此同时使用对抗性学习的方法训练模型,以达到提高推荐模型鲁棒性的目的;其次设计了一种高效的算法用于求解模型所需的参数;最后在Reddit和Pinterest两个公共数据集上进行测试。实验结果表明:1)在相同参数设置下,与现有算法相比,所提方法的AUC,Precision和Recall指标均有明显的提高,验证了其可行性与有效性;2)该算法不仅增强了推荐性能,还提高了模型的鲁棒性。更多还原

【Abstract】 The recommendation system can recommend relevant information and commodities to the user according to the user’s hobbies and purchase behavior.As user-generated content UGC gradually becomes the mainstream of current Web applications, recommendations based on UGC have also received widespread attention.Different from the binary interaction between user and item in traditional recommendation, the existing UGC recommendation adopts collaborative filtering method to propose a ternary interaction between consumer, item and producer, thereby improving the accuracy of recommendation, but most of the algorithms focus on the recommended performance and ignore the research on robustness.Therefore, by combining the ideas of adversarial learning and collaborative filtering, a collaborative filtering recommendation algorithm based on adversarial learning is proposed.First, the adversarial disturbance is added to the ternary relationship model parameters to make the performance of the model the worst.At the same time, the adversarial learning method is used to train the model to achieve the purpose of improving the robustness of the recommendation model.Secondly, an efficient algorithm is designed used to transform the parameters required by the model.Finally, it is tested on two public data sets generated by Reddit and Pinterest.The experimental results show that under the same parameter settings, compared with the existing algorithms, the AUC,Precision and Recall indicators of the proposed algorithm have been significantly improved, verifying its feasibility and effectiveness.The algorithm not only enhances the recommendation performance, but also improves the robustness of the model.更多还原