Research Article
Personal Recommendation in User-Object Networks
@INPROCEEDINGS{10.1007/978-3-642-02466-5_23, author={Tao Zhou}, title={Personal Recommendation in User-Object Networks}, proceedings={Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009. Revised Papers, Part 1}, proceedings_a={COMPLEX PART 1}, year={2012}, month={5}, keywords={Infophysics Personal Recommendation Bipartite Networks User-Object Networks Diffusion}, doi={10.1007/978-3-642-02466-5_23} }- Tao Zhou
Year: 2012
Personal Recommendation in User-Object Networks
COMPLEX PART 1
Springer
DOI: 10.1007/978-3-642-02466-5_23
Abstract
Thanks to the Internet and the World Wide Web, we live in a world of many possibilities we can choose from thousands of movies, millions of books, and billions of web pages. Far exceeding our personal processing capacity, this excessive freedom of choice calls for automated ways to find the relevant information. As a result, the field of information filtering is very active and rich with unanswered challenges. In this short paper, I will give a brief introduction on the design of recommender systems, which recommend objects to users based on the historical records of users’ activities. A diffusion-based recommendation algorithm, as well as two improved algorithms are investigated. Numerical results on a benchmark data set have demonstrated the advantages in algorithmic accuracy.
