A hybrid algorithm for recommendation Twitter peers

This paper presents a hybrid algorithm in the area of peer recommendation in Twitter. Due to the big data issue on Twitter, we define a filtering strategy to reduce the number of candidates who might be recommended to the target user. Meanwhile, we refine the content-based similarity and graph-based similarity algorithms proposed by other academics. Moreover, we define a user model and weighting formula to leverage these two algorithms. According to the similarity degree between the candidates and the target user, we recommend the top k most similar candidates to the target user as our focused peers. In order to evaluate the effectiveness of our proposed algorithms and other algorithms, we conduct a personalized survey and employ measurements like recall, precision and F1 metric. The evaluation results demonstrate that our hybrid algorithm is better than the pure content-based similarity algorithm and pure graph-based similarity algorithm.