The Personal Information Recommendation System Base On Users' Interest

With rapid development and popularity of the Internet, It is more and more convenience to find the needed information for everyone. The Internet information is so great that people often lost themselves in it. Therefore how to provide the personal information to people is becoming the important problem that the reaseachers care for.The information recommender system based on information filtering technology communicates with the users, and provide different information for different users .In information field, every user have his steady perennial requirement. The requirement of users can contribute to filter the useless information and help users to select the needed ones.Recommendation systems have been very successful in both research and practice, but some challenging problems remain in this field. Aimed at the main challenges of recommendation systems, this thesis explored some key technologies of recommendation systems. The main research works in the thesis cover two aspects:1). For the improvement of the recommendation quality, we proposed a collaborative filtering recommendation algoritm based on the rating of items' character. In the algoritm, items are parsed to characters, and as contributes to improve the system performance when rating data is sparcity. In addition, the cluster of the users is used to minish the searching range for the active user. The experiment results suggested that this method could efficiently overcome the extreme sparcity of user rating data and provide better recommendation results than traditionalcollaborative filtering algoritms2). For making use of the advantages of the collaborative-filtering algoritms and content-based filtering algoritms, we proposed a multi-filtering recommendation algoritm using ant colonies. The experiment results suggested that the system's performance is improved after the combine of collaborative-filtering and content-based algoritm. The algoritm contain the advantages of content-based algoritm (covering all of documents and and the rating of early users). At the same time, the recommendation is still accuracy even if the increase of the user rating, as is also the advantage of the collaborative-filtering algoritm.