Research On The Key Problems Of Collaborative Filtering Recommender System

With the rapid development of Internet, the human race has entered the information society and the network era. Internet provides people with more and more information and services and it has broken the limit of space and time of traditional life and learning. People can shop on the Internet conveniently and study via Internet whenever and wherever. However, people have to face enormous data and useless information when they enjoy the convenience brought by Internet. This is the famous "information overload" problem and "information confusion" problem. Personalized recommender system has emerged in response to this challenge and it provides users with personalized recommendations of items which are likely to fit their needs.Collaborative filtering algorithm is a core technology of personalized recommender systems, and it is also one of the most widely used and successful technologies. But with the expansion of system, collaborative filtering algorithm is facing many challenges. This paper concentrates on some key problems faced by collaborative filtering algorithm, which are the sparse problem, the new user cold-start problem and the scalability problem. This paper does some research on the following aspects:1) In order to solve the problem of low recommendation precision faced by item-based collaborative filtering algorithm when the user-item rating matrix is sparse, this paper proposes a relation-based collaborative filtering (RBCF). This algorithm points out the shortage of traditional item-based collaborative filtering algorithm when generating candidate item set. Then it proposes to generate candidate item set using item correlation matrix which is calculated according to the thought of association rules. At the same time, in order to alleviate the inaccurate of similarities when the user-item rating matrix is sparse, this algorithm adjusts the Pearson correlation. Finally, experiments show that the proposed algorithm could greatly improve the performance of recommendation. In addition, the propose method can also reduce the size of candidate item set obviously, which alleviates the scalability problem of collaborative filtering algorithm.2) This paper points out the shortages of the traditional collaborative filtering algorithm when facing the new user cold-start problem, and then proposes a filling-based collaborative filtering altorithm (FBCF) basing on the traditional item-based collaborative filtering altorithm. The proposed algorithm proposes a partial filling method to extend the new user’s rated item set with a high efficiency. Besides alleviating the new user cold-start problem, another advantage of the proposed algorithm lies in with no need for additional information except the basic user-item rating matrix. In order to evaluate the proposed algorithm, experiments based on the well-known dataset were conducted to compare the FBCF algorithm and other benchmark algorithms, and the results show that the FBCF algorithm has an obviously better performance.3) With the expansion of the system and the increase of the number of users, the collaborative filtering algorithm not only faces the sparse problem but also the scalability problem. The expert-based collaborative filtering algorithm shows a new solution to this problem, and it can solve the scalability problem effectively while maintaining a relatively high prediction accuracy and precision. But it brings another problem—how to select the experts effectively. Therefore, this paper puts forward a new collaborative filtering algorithm incorporated with cluster-based expert selection (CBES). In the algorithm, this paper firstly redefines the expert and maps it to a simple and easy measurement metrics. Then this paper clusters the users with the same interests as a group, and selects representative experts from each group. Finally, this paper compared the proposed algorithm to the traditional user-based collaborative filtering and the algorithm proposed by Xavier, and the results show that the proposed algorithm can get well performance on prediction accuracy and recommendation precision.4) Based on the above research, this paper discusses research and implementation of recommender system for adaptive learning services. This paper designs and implements a recommender system for country education platform (CEPRS) based on rural education platform. The system implements the proposed algorithms, provides personalized service for online learning and makes online education platform get a big step forward on user centric. This paper introduces the architecture and the main function modules of the system, and gives out a brief explanation to the implementations of the proposed algorithms. This system has good portability and maintainability characteristics.