Research And Implementation Of Personalized News Events Recommendation System

As the popularization of Internet is accelerating the innovation of journalism, getting news online has become one of the most important ways that people obtain realtime information. However, facing a large amount of netnews, it is usually hard for people to find out the news that they are interested in rapidly. Based on this situation, News Event Detection Technology has come out, which is able to dig out hot topics from the Internet and assist Internet users in understanding them. Besides, Individualization Recommendation Technology can excavate interests of Internet users' according to their behaviors, and then recommend them news that they are possibly interested in.News Event Detection Technology and Individualization Recommendation Technology have both been widely used in information area at present. However, the hot topics that are found out by News Event Detection Technology can hardly take each user's fancy. In the meantime, Individualization Recommendation Technology could only recommend single news to users rather than the topics and events they are interested in. Thereupon, this dissertation will present an Individualization News Events Recommendation Technology, which is a combination of News Event Detection Technology and Individualization Recommendation Technology and able to dig out news events as well as recommend them to users individually. Main works for this research project are listed as below.(1) Implementing an improved hierarchical clustering algorithm based on the LDA topic model. When calculating the similarity of different news, this algorithm will use weighed summation of certain proportion based on VSM model of TF-IDF and LDA topic model. When calculating the distance between different cluster, this algorithm will bring in the distance of different cluster's centers as well as the longest distance between news in different cluster, which will make the result more accurate. The outcome of experimental research tells that the algorithm referred in this dissertation can largely bring up the calculation accuracy compared to traditional hierarchical clustering algorithm.(2) Implementing a news event recommendation algorithm based on hybrid recommendation. This algorithm will firstly construct an interest model of target users respectively according to multiple characteristics of events and behaviors of the users. Then, based on VSM model and LDA topic model, the similarity among different users will be calculated through weighed summation of certain proportion. Thus, neighbor user groups of the target users can be obtained, which are useful in integrating preferences of the target users and digging out their interests. Finally, the news events that the target users might be interested in will be recommended to them. Experiments of this dissertation have proved that this algorithm has high accuracy and recall rate.(3) According to realistic demand, this dissertation will develop an individualization news event recommendation system. Experiments have indicated that this system is able to achieve presupposed functional objectives with high stability and efficiency.