Community Recommendation System Based On Machine Learning

With the rapid development of Internet technology,we are gradually entering the era of big data.In recent years,many communities,BBS,Internet social,question and answer platform etc.are rising rapidly.Because of the rapid growth of the amount of data on the Internet,which makes users fail to select the information needed quickly.Therefore,It becomes quite urgent to help users to search the information which meets users' preferences quickly.Information recommendation technology is the important means to realize this requirement.By mining,analysis of user behavior data,as well as the comment text data in the community,recommendation system can quickly locate the information which user needs,and provide users with reliable results.As a result,not only the time to retrieve information can be greatly reduced,but also the user experience was of the product was improved.Therefore,the recommendations have played an important role in improving user satisfaction with product experience,also provided users more convenient and faster life.Collaborative filtering recommendation technology is one of the most common recommendation methods.By reference to the history of user behavior and articles' different attributes to predict users' favorite articles,we can select highest scores articles to recommend to the user.The research work of this thesis is to put forward to improve and optimize the traditional collaborative filtering technology.Although traditional collaborative filtering technology is widely used,it has following disadvantages:(1)sparsity of the rating matrix.In the large-scale recommendation system,both the number of user and number of item are very large.But the number of items in fact users expressed preferences is quite few.That is to say,the known data is very sparse.Sparsity leads to the deviation in the process of calculation,such as calculating the similarity between users or items when making recommendations,but sparsity makes the calculating of similarity between users or items inaccuracy.(2)Cold Start.New user with few rating record,so it is difficult to analyze his preference and it is impossible to make effectively recommendation for him.(3)Homogenization.Users' interests are diverse,the explicit or implicit expressed interest is very limited,the recommendation method based on content filter recommends items which matches with current interest,therefore,this recommendation results homogenization,it is hard for items which users didn't express preference but actually interested to get recommendations.In view of the above problems of the traditional collaborative filtering,this thesis puts forward the corresponding improved algorithm:Latent Factor Model(LFM)connects user interests and articles by implicit features.LFM splits the two-dimensional users-articles matrix into user-hidden theme matrix and hidden theme-article matrix,which avoids the occurrence of zero that makes similarity inaccuracy.Use provided data such as age,gender in user registration to do coarse granularity personalized recommendation.Asking the user to feedback of some items when logging in,collecting user's interest in these items,then recommends the similar items to the user.In feature extraction and similarity calculation,the use of TFIDF,Word2 Vec and some other methods will be applied in the research.Through the experimental analysis,it is found that introducing category features can improve the accuracy of the article recommendation.To verify the recommend effect of combined model,we collected some real data from enterprise as datasets,by comparing various algorithms,and experiment on a variety of performance metrics,recommendation algorithm based on combined model achieved great score in the above datasets.The accuracy,recall and diversity have been improved.As a result,the algorithm was superior to other traditional recommendation algorithm was proposed,which proved the effectiveness of the recommendation algorithm based on combined model.