基于频繁主题集偏好的学术论文推荐算法

针对学术论文推荐中项目冷启动问题，提出了一种基于频繁主题集偏好的协同主题回归模型。该算法考虑到用户在选择学术论文时对研究热点的偏好，使用频繁主题集代表研究热点，将用户对研究热点的偏好表示成用户对频繁主题集的偏好。通过潜在狄利克雷分布主题模型挖掘得到论文—主题概率分布矩阵，并筛选出论文中概率较高的主题；然后挖掘出频繁出现的主题集合，并得到论文—频繁主题集矩阵；最后在预测未知评分时融入用户对频繁主题集的偏好。在CiteULike数据集上的实验表明，相比于矩阵分解模型和协同主题回归模型，该算法在召回率、准确率和RMSE三个指标上都有所提升。

Academic paper recommendation algorithm based on frequent topic sets preference

This paper proposed a collaborative topic regression model based on the preference for frequent topic sets to address the item-cold-start problem in academic paper recommendation. The algorithm took into account the user''s preference for research hotspots when selected academic papers, and used frequent topic sets to represent research hotspots. So, it expressed user''s preference for research hotspots as the user''s preference for frequent topic sets. Firstly, it obtained the papers-topic probability distribution matrix through LDA algorithm and filter out the topics with higher probability. Then, the algorithm mined the frequently-occurring topic sets and gets the relationships between papers and frequent topic sets. Finally, it used the user''s preference for frequent topic sets for the prediction of unknown scores. Experiments on CiteULike datasets show that the algorithm improves the recall, accuracy and RMSE over the matrix factorization model and the collaborative topic regression model.