A Study Of Recommendation Algorithms Based On User Dynamic Internet Preference Based On Reviews And Ratings

In recent years,with the development of Internet technology,recommendation system technology applied to various fields,also with suitable for different areas of the recommendation algorithm.And recommendation algorithm based on user comments and ratings have been applied to many users comment on information in the online recommenation system.The center of the recommendation system is recommend user need to do,so build the user interest preference model is an essential step.When constructing user model,user review of large amount of data and there is no central topic,if comments without any data processing and direct use in building model,general will lower the efficiency of recommendation and not effective to quickly capture the user's preferences.Another important factor to influence the user preference is time.Because the user's preferences is not a layer of constant,will change over time,but the previous scholars on the analysis of user reviews and user ratings without considering the factors,which cause poor recommendation quality,recommend the real-time performance.Comments from users and user ratings,therefore,to extract the user's preferences,and through the comments timestamp to carry on the dynamic analysis of the user preferences will have very important theoretical research and practical application value.User preference model based on the analysis of user reviews and ratings to obtain information,but the amount of data is great,access and rapid analysis of the data quantity of information is very difficult,hard to implement.Therefore,through summarizing the related research found that the user preference model can generally by keywords representation and theme representation.Keywords notation refers to recommend system described by a set of keywords to represent the user's preferences model of user preferences.Theme notation refers to recommend system adopts the theme of the user's interests type word to build the user interest model.Because this article research needs,this paper adopts the theme representation model building user preference.However,at this point the user preference model is static,to track dynamic user preferences,further studies are needed.In view of the above challenges,this paper analyzes the current recommendation system of domestic and foreign research present situation and the main recommendation technology,the advantages and disadvantages of the existing of the recommendation algorithm based on user comments and user ratings on innovation.In this paper,the main research work is as follows:(1)based on the analysis of user reviews build preference model,to review the data set was carried out based on the theme of the LDA model,draw a review document-topic distribution vector and theme-word distribution vector,to facilitate the next step to build user preference model accurately.(2)considering the user's preferences change over time,so on the basis of the model based on the theme,joined the nonlinear forgotten time function,put forward the model based on the theme and time forgetting function of mixed preference model.(3)when considering the user for emotional attitude of items,to indirectly measure by user ratings.But different user ratings measure the effect of different time period,therefore,joined the index time function on user ratings,reviews the shorter the time interval,scoring the smaller the impact on the user preferences.(4)carry out collaborative filtering algorithm based on user comments and user ratings,and Amazon website in the six experimental data set.Through the research work of this article,finally draw the following conclusions:(1)when large amounts of data,in order to obtain user information resources,and rapid analysis in this article,through the LDA information clustering topic model,quickly get the user's preferences.(2)through the verification of the data set,the user preference model based on user reviews and ratings to join in the review time can improve the prediction accuracy.(3)on the same data set,the complete algorithm is proposed in this paper and other scholars put forward algorithm,this algorithm has a lot to improve on the forecasting accuracy.