Optimization And Implementation Of Collaborative Filtering Recommendation Algorithm Based On Sentiment Analysis

Recommendation systems,offering personalized product recommendations based on users' behavioral characteristics,interests,and preferences,are widely applied in platforms of online shopping as well as social networking by videos and news,among others.The recommendation systems mainly have two roles.First,they solve the problem of information overload,helping users quickly discover the contents they are interested in.Second,they solve the problem of burying long-tail information,enabling more high-quality products to be displayed.For the recommendation systems,the collaborative filtering recommendation algorithm is widely applied,but many problems still exist:how to solve the problems of data sparsity as well as one-sidedness and timeliness of recommendation results remains to be studied.Moreover,further research is needed to improve the recommendation efficiency while improving the precision of recommendation results.Therefore,this paper aims to optimize the collaborative filtering recommendation algorithm by using sentiment analysis and cluster analysis.The paper delves into the following areas:(1)In terms of improving the precision of recommendation results,the sentiment analysis method based on Bayesian classification is adopted to perform sentiment analysis on the contents of user comments.Using the sentiment values and product feature vectors obtained from users,the paper constructs the evaluation matrix of user-product feature sentiment values,which reduces the sparsity of the evaluation matrix and improves the precision of recommendation results.(2)In terms of improving recommendation efficiency,k-means++is employed to perform cluster analysis on users' emotional preference vectors,and to calculate the evaluation feature vectors of different types of users.In addition,the improved similarity calculation method is used to predict target users'behaviors,which significantly boosts the recommendation efficiency.(3)In terms of model comparative analysis,with the original collaboration as a reference,comparative experiments are conducted on cluster collaboration based on sentiment analysis,user-product cluster collaboration,and cluster collaboration of user-product features.During the process,different numbers of training sets and TopN quantity conditions are applied in each experiment,and the experimental results are compared and analyzed from various perspectives.The finding is that the proposed model can optimize the precision of recommendation results and recommendation efficiency.To sum up,combining sentiment analysis,cluster analysis,and collaborative filtering,this paper constructs an optimization model,which is applied to book recommendation,and conducts multiple comparative experiments.The results show that cluster collaboration based on sentiment analysis achieves the best performance in terms of precision,recall,and F-measure.Finally,based on the conclusions,this paper summarizes and proposes further research directions to provide suggestions for the optimization of recommendation models.