Recommendation Algorithm Based on Graph-Model Considering User Background Information

Author

Wang, Ziqi ; Zhang, Ming ; Tan, Yuwei ; Wang, Wenqing ; Zhang, Yuexiang ; Chen, Ling

Author_Institution

Sch. of Electron. Eng. & Comput. Sci., Peking Univ., Beijing, China

fYear

2011

fDate

18-20 Jan. 2011

Firstpage

32

Lastpage

39

Abstract

With the development of information technologies and increase scale of digital resources, personalized recommendation systems have come into the big picture of web2.0 technology. This paper proposed a graph-based recommendation algorithm using the user-resource rating data to construct a graph model and improves the model by adding user background information. The Random Walk with Restarts algorithm is applied to generate the final recommendation set. The improvement in accuracy on sparse data is illustrated by the experiments on the Movie Lens data set, comparing with the collaborative filtering algorithm.

Keywords

Internet; graph theory; information resources; personal computing; recommender systems; MovieLens dataset; Random Walk; Restarts algorithm; collaborative filtering; digital resources; graph-model; information technologies; personalized recommendation systems; sparse data; user background information; user-resource rating data; web2.0 technology; Algorithm design and analysis; Collaboration; Computer science; Motion pictures; Protocols; Social network services; Symmetric matrices; Collaborative Filtering; Personalized Recommendation; Random Walk with Restarts; User Background Information;

fLanguage

English

Publisher

ieee

Conference_Titel

Creating, Connecting and Collaborating through Computing (C5), 2011 Ninth International Conference on

Conference_Location

Kyoto

Print_ISBN

978-1-61284-390-2

Type

conf

DOI

10.1109/C5.2011.11

Filename

5936689