On Collaborative Filtering Algorithm And Applications Of Recommender Systems

With the fast development of Internet and applications of E-Commerce, more and more information swirles in the net. To get the right information from the information sea has become one of the key issues nowadays for the researchers, experts and the Internet users. Personalized Recommender Systems emerge under the background of this, which becomes the research focus in the domestic and overseas.Collaborative filtering algorithm is the most key technology in the personalized recommender systems, which has got the most success and wild applications. This dissertation takes collaborative filtering algorithm of personalized recommender system as the research project to deal with the sparsity problem, cold start problem and trust problem, etc. Research work are taken as following:(1) Review the research development of personalized recommender system and discuss the concept of personal from the angle of recognization and phsycology to give some advice for the configuration of personalized recommender systems; give a general analysis of recommender technologies and indicate their individual characters and application fields; analyze the system constructure and modules to give some instructions for applications; at last, give a division of the collaborative filtering and indicate their challenges for research work.(2) Put forward an improved collaborative filtering algorithms based on nearest neighbor rating matrix for the sparsity problem of collaborative filtering algorithm. First, get the nearest neighbor rating matrix through Cosine similarity calculation metrix and produce a prediction for items according to similarity between users and neighbors' ratings. Then all nearest neighbors' prediction ratings forms a virtual nearest neighbor rating matrix. This matrix takes on a similarity with the active user rating and the problem of prediction for the active user could be transfer to the matrix. Compared with history rating matrix, virtual nearest neighbors' rating matrix has small scale and contains the most useful information. At last, a prediction based on weights of similarity between users and ratings is produced. Experiments improved the improved algorithm this paper advanced and especially when the rating matrix is very sparse, prediction accuracy is much better.(3) Advanced a hybrid recommender algorithm based on the combination of collaborative filtering and item keywords based prediction. Analyze the combination problem of item keywords based prediction and collaborative filtering when item keywords information is not adequate. Through the content abstraction of the items, items in the recommender system are represented by 0 and 1. After this users' prediction for the items are got by using winnow algorithm. To guarantee the accuracy of the prediction matrix, two constraint parametersα_i (which indicate the rating number of each user) andβ_i (number of the prediction which is accurate ) are used to filter the prediction. Only those users' predictionwhich is accurate could be into next step filtering——collaborative filtering. Experimentimproved that the hybrid recommender algorithm with the constraint parameter is much superior to the traditional collaborative filtering algorithm and the algorithm without the constraint parameters. The hybrid algorithm this dissertation put forward improved the cold start problem from certain extent.(4) Put forward an assumption that introduce social trust into recommender systems andconstruct a collaborative filtering algorithm based on trust. Configuate two trust models——global trust and local trust under the foundation of formatlate description of the trust and then indicate their effective parameters. Through this two calculatable trust model, user trust could be measured and a collaborative filtering algorithm based on trust is then advanced. The expeiriment proved the superiority of the algorithm. In the experiment the distribution of the trust of two trust model and the distribution of similarity are also be demonstrated which could be safely concluded that trust is a parameter that affect the final recommendation which is very different between the similarity. So the assumption advanced at first is very meaningful.(5) Construct a general model of the personalized recommender system based on CBR. Comparing the difference between CBR and CF, the similarity and difference is generalized. Then CBR and CF are combined together which using CBR to improve the learning ability of the personalized recommender system. A personalized film recommender prototype system is also developed at last which could prove the models and algorithms this dissertation put forward.