The Research On Key Technologies Of Recommender System

With the development of the Internet, the web provides more and more information for users, while the structure of the web has also become more and more complex. This situation has made it substantially more difficult for users to find the information they need from the vast amount of materials available on the Internet. The recommender system provides information filtering for a user by predicting the particular user's preference, and it can apply knowledge discovery techniques to make personalized recommendations to help the user quickly find the desired information. At the same time, the recommender system can enable enterprises to achieve the objective of personalized marketing, which can improve sales and generate more profits. In addition, with the popularization of personalized service, the recommender system is widely used on a growing number of web sites, especially in the E-Commerce platform. Because of its great potential for development and applications, the recommender system has become an important research area in web intelligent technologies and attracted significant attention from researchers.Although the development of recommender system has been successful in both research and applications, a number of challenging research problems still exist. To address these challenges, this dissertation explores and studies some key technologies of the recommender system, such as the design of novel algorithms with better recommendation quality and enhanced privacy protection technology. In particular, data mining and machine learning techniques are incorporated into the recommender system. Technologies for enhanced real-time recommendation, improved recommendation quality and strengthened privacy protection in the recommender system are investigated.The main research results of this dissertation are as follows:First, the performance of collaborative filtering systems degrades with increasing number of customers and objects. To reduce the dimensionality of filtering databases and to improve the performance, non-negative matrix factorization (NMF) is proposed. Theoretical analysis proves that NMF-based collaborative filtering can accelerate the process of recommendation generation to satisfy the demands of real-time recommender system. Experimental results show that NMF-based algorithm can improve the performance of collaborative filtering systems in both the recommendation quality and the efficiency of recommendation generation.Second, the number of objects in the recommender system is generally very large, and individual users are only able to evaluate a small fraction of all the available objects. As a result, the lack of the overlap of objects rated or evaluated by different users can prevent the recommender system from recognizing the otherwise obvious similarity among different objects if each of these objects is rated by a different individual. This is the "similar but not identical" problem which can seriously affect the quality of recommendation results. Therefore, the multi-layer similarity concept is presented and a multi-layer similarity evaluation procedure is established for the recommender system. The experimental results illustrate that the multi-layer similarity evaluation can improve the accuracy and consequently the quality of the recommendations.Third, to overcome the speed bottleneck of collaborative filtering algorithm used for generating recommendations, a collaborative filtering algorithm based on clustering basal users is described. The algorithm separates the process of recommendation into offline and online phases. In the offline phase, the data of basal users are preprocessed, and the basal users are clustered; while in the online phase, the nearest neighbors of an active user are identified according to the basal user clusters, and the recommendation to the active user is generated. During the course of recommendation generation, the multi-layer similarity evaluation is used. Experimental results show that the presented algorithm can improve the performance of collaborative filtering systems in both the recommendation quality and efficiency.Finally, the recommender system operates by collecting rating or evaluation information for objects and matching users who share the same interests or tastes. This is potentially a serious threat to individual privacy because most online systems collect preferences of users which include their private information. So more and more users and researchers are concerned with the privacy protection in the recommender system. In this dissertation, a new privacy protection algorithm is presented. In this algorithm, randomized perturbation techniques are applied during the course of user data collection, and the collected data are further processed by NMF, which enables the protection of sensitive information. The algorithm produces the recommendation based on the privacy protected users'data. Both the theoretical analysis of the algorithm and experimental results demonstrate that the algorithm can not only protect users'privacy, but also generate recommendations with satisfactory accuracy to meet the needs of the recommender system.