Research And Implementation Of Text Recommendation System Based On Content And Social Network

Recommendation model is a hot trend in Internet technology research in recent years.However,traditional content-based recommendations,social network-based recommendations and collaborative filtering recommendations all have their own shortcomings.Therefore,we propose a content-based and social network-based recommendation model(RMBCS).The main content of this paper are:(i)The similarity calculation between long text and short text.At present,there is no formula to calculate the similarity between short and long text.So,this paper proposes a generation distance.After text classification,preprocessing and feature extraction,we use features to compute the generation distance as the similarity between short and long text.(ii)Selection of users'nearest neighbor groups in social networks.In a system with a large number of users,the selection of the users' nearest neighbor group takes much time.In order to select the user's nearest neighbor group quickly and conveniently,this paper proposes a new method to find the nearest neighbor group from the users'social network.(iii)Design and implementation of text recommendation system based on content and social network.The above two points are embedded into the recommendation system.By illustrating the recommendation method of the text recommendation system based on content and social network,the system's needs analysis and overall design are carried out,and each module of the system is designed in detail to complete the design and implementation of the system.Finally,the system is tested.