基于联合相似度的协同过滤推荐算法分析.docx

ABSTRARCT
With the rapid development of the , Web has e the main way for people to access to information. While merce is widely spreading among people, the issue as to promote users with useful information from the expending information has e a big concern. The emergence of search engine satisfied the need of information retrieval in a certain degree; however, it cannot meet the users’ demand from different fields and different levels. Therefore, personalized mendation technology is born form the need of information searching, it is one model of personalized service and its nature is information filtering.
In order to e information overload, mender systems have e a key tool for providing users with personalized mendations on items such as movies, music, books and news. Intrigued by many practical applications, researchers have developed algorithms and systems over the last decade. The classic and widely used mendation method is collaborative filtering. The goal of collaborative filtering system is to suggest new items or to predict the utility of items for users based on the users’ previous likings and the opinions of the other like-minded users. But there still exist many problems, such as the cold-start problem and the data sparsity problem.
A collaborative filtering mendation method based on Co-similarity brings methods of work analysis into collaborative filtering. We propose a collaborative filtering algorithm based on Co-bines multiple similarity matrices from unipartite work, user-item work and the work based on their behavioral similarities that puted by using navigational patterns. Moreover, we propose an effective weighting strategy of SRNs influence based on their structured density. We perform an parison of the proposed method against existing rating prediction and product mendation algorithms using douban dataset. Our experimental results show that the proposed method is more effective.
Keywords: mender System, work, Collaborative Filtering, Co-simi