一种结合主题模型的推荐算法

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-04-12 Cooperative journals: 《计算机应用研究》

曹占伟 胡晓鹏

Abstract： In order to solve the problem of cold start and data sparsity for traditional collaborative filtering recommendation algorithm, and the accuracy of similarity measurement, this paper proposed a matrix decomposition recommendation algorithm based on the LDA theme model. Firstly, it uses the improved LDA algorithm to output the project-topic distribution, using the perplexity as the modified function of the subject number; Secondly, it calculate the similarity matrix of the project based on the cosine similarity and the KL divergence, combineing the obtained similarity matrix with the original scoring training set to output the pre score, and then fills the preliminary score to the training set. Finally, it input the training set to ALS matrix decomposition algorithm to get the recommended results. The experimental results of the MovieLens data set show that the proposed algorithm can get a smaller MAE values than the traditional ALS algorithm under different implicit parameter settings and it greater than traditional recommdation algorithm . The experiment shows that the results of the ALS algorithm are better than other algorithms by integrating the LDA theme model.

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