Integrating with learning to rank methods, the authors propose a movie ranking prediction model by mining and analyzing the data from movie media websites, which includes extracting and expanding features related to ranking prediction as well as dividing and aligning ranking labels etc. Experiment results show that the proposed model effectively improves the performance of the movie ranking prediction task, which can benefit the cinemas to arrange the number of screenings properly. The model can also provide high quality recommendations to movies for the fans.

The authors extended the traditional “bag of words” idea. Every document was regarded as “bag of sentences”. The dependency relationship of the words was obtained from the “bag of sentences” and verbose queries by dependency parsing. According to the matching degree of the dependence relationship, the similarity scores between verbose queries and documents was obtained. Finally, the initial results were re-ranked. Experiment on a standard TREC corpus shows that new approach can improve retrieval effectiveness for verbose query and the low recall rate. For the low recall rate, the MAP and P@N have a significantly improvement.