Improved Collaborative Filtering Recommendation Algorithm Based on Weibo Content

Taking Weibo content as the research object and aiming at the disadvantages of only popular recommendation for Weibo, we propose personalized recommendation scheme. The improved user-based collaborative filtering algorithm is used to give a recommendation list. In this paper, the keywords extraction technology is used to extract the keywords of Weibo content as the project, and the results of the sentiment analysis algorithm are used as the scores to form the project score matrix. Then, the similarity of the item and the score are calculated by the cosine similarity and the Jaccard coefficient, respectively, the final similarity result is obtained by weighted fusion. The experimental results show that the improved algorithm proposed in this paper can effectively solve the problem that the microblog content has no scoring system, which helps to improve the performance of the recommendation system and obtain better recommendation results.