A movie cold-start recommendation method optimized similarity measure
Peng Yi, Yang Chen, Xiaoming Zhou, Chen Li · 2016
With the explosion of Internet information, recommender system plays an increasingly important position in online video searching. Collaborative filtering technique the most popular recommendation algorithm is inefficient in cold-start scenario. In this paper, we focus on new movie cold start problem and aim to bridge the gap between movie labels and movie similarity. A useful approach is proposed to optimize the movie similarity measure by computing the similarities among directors and actors. We verify the usefulness of our method on MovieLens-1M data sets. The experiments show that our method has a significant improvement on movie cold start problem.