Research On Improvement Of Collaborative Filtering Clothing Recommendation Algorithm

As one of the most successful personalized recommendation technologies until now, collaborative filtering technology obtains widely attention of researchers, and becomes the key issue need to be studied in the field of recommendation. This thesis has carried on the beneficial exploration and the research, mainly aiming at collaborative filtering technology, has brought in time forgetting technology, user preference and user feature, and proposed collaborative filtering algorithm which is based on the features of user attribute and the changes of user interest, precisely aiming at the defect of collaborative filtering technology. Moreover, this research has combined user interest, time effect, user preference for item and user feature into an organic unity, and made them supplement the merit mutually to improve the collaborative filtering algorithm which is based on user. The main work which the thesis has completed included:â‘ To the recommendation system, this thesis has conducted the thorough research,including concept, research content and composition. In addition, it has introduced various recommendation technologies in details, and analyzed the superiorities and the insufficiencies of various types of recommendation technologies, as well as the characteristics of the existing typical recommendation system example. On the basis of this, the thesis lays emphasis on the research of the mainstream technology in the recommendation field---ollaborative filtering technology, and makes a detailed and comprehensive introduction of collaborative filtering algorithm.â‘¡Considering the dynamic change of the user interest and drawing lessons from the forgetting rules of human being, this thesis has brought in forgetting technology, and put forward the improved nonlinear forgetting function on the basis of the current research achievement. According to the different temporal sequences it appears, this research entrusts each characteristics of user interest with different weight numbers, attenuates the item grading in different speeds according to time t, and change the degree of contribution of grading to recommendation results in different time.â‘¢this paper improve the algorithm with two new methods:Predict score incorporated with time information in order to reflect the user interest changes; Recommend according to the score data by adding the weight information determined by the project life cycle. Experimental results show that the proposed algorithm outperforms the traditional item in accuracy and freshness.