A Recommendation Model Based on Content and Social Network

Recommendation model are a popular trend in recent research in Internet technologies. However traditional content-based recommendation, social network-based recommendation and collaborative filtering recommendation have their own shortcomings. To overcome them, we proposed a recommendation model based on content and social network (RMBCS). First, we proposed a new distance to calculate the text similarity between long text and short text. Then, we proposed a new method to find the nearest neighbor group from user's social network quickly and conveniently. At last, we recommend the texts which user's nearest neighbor group had read to the user. Experimental results indicate that our model has a better performance.