Microblog Retrieval Results Re-ranking Using Graph Model Based Decision

As a typical short text, microblogging retrieval suffers from the problem of the insufficient samples both in users’ query and documents that makes the probabilistic-like models unreliable. To remedy this problem, a graph model was designed and implemented based on topic clustering algorithm to re-rank microblog retrieval results. The graph model was built by the content similarity between micro-blogs. By comparing the cosine similarity, the dice coefficient, and the one-way dice coefficient with the experimental results. Results show that the performance of the search depends on the ratio of related topics, therefore decision tree algorithm was used to remedy the influence of the ranking position relevant topics.