Research on Movie Recommendation Algorithm Combining Movie Attributes and Interactive Information
As an information filtering system,the movie recommendation system analyzes the historical data of target users to recommend movies that they may like.In order to improve the recommendation quality of movie recommendation systems,this paper proposes a movie recommendation algorithm U-LFM which combines movie attributes and interactive information.The algorithm first tackles the sparsity problem of the user rating matrix by using the traditional LFM algorithm to fill the matrix.Secondly,to address the issue of singularity in the influencing factors of recommendation,it combines movie attributes and user interactive information to increase the diversity of movie recommendation system.Finally,by comparing the precision and recall rate of other movie recommendation algorithms under the same registration conditions through experiments,it is verified that U-LFM has a high quality of movie recommendation.
万方数据
维普
