Research And Implementation Of Wideband High Resolution Frequency Synthesizer

With the rapid development of the Internet,every day there are vast numbers of information generated.In the face of large amounts of information,people tend to feel at a loss,therefore,recommendation system came into being.The purpose of the recommendation system is to proactively provide users with items or resources of interest without the user's active search.After 20 years development,recommendation system has been deep into people's lives in every aspects,such as e-commerce,news recommended,movie recommended.The existing movie recommendation system is mainly popular recommendation and related recommendation.Popular recommendation easily leads to the Matthew effect.Related recommendation accords with user's preferences to a certain extent,but the recommended results have a low level of personalization,which means different people will see the same recommended results on the same play page.Collaborative filtering is one of the most successful and widely used recommendation strategies in the field of recommendation.This thesis is based on collaborative filtering algorithm to improve.The level of user rating expresses the level of preference for the movies,and the user's tagging behavior expresses the user's preferences,so that the combination of the two can effectively improve the personalization of the recommendation results.In this thesis,first step is analyzing user's behavior data and establishing the initial user behavior data model.At the same time,taking into account user's preferences will change over time,the time decay factor is introduced to simulate the change of user's preferences on the whole time axis.After that,the data model is used to calculate the similarity level between the users to obtain a candidate pool of recommended movies.In the step of forecasting movies' score,establishing links between tags and movies reference to term frequency inverse document frequency to improve prediction accuracy.In order to verify the effectiveness of the proposed algorithm,choosing hit-rate and hit-rank as evaluation criteria to top-N recommendation.It is proved that the proposed algorithm's hit-rate and hit-rank are higher than the original collaborative filtering algorithm in the case of recommending the same number of movies.Finally,this thesis designs and implements a movie recommendation system based on the proposed algorithm.The requirements and design of the system are described.The system is implemented with SS2 H framework,and the main data table and functional interface are given.