%0 Generic %1 shams2016graphbased %A Shams, Bita %A Haratizadeh, Saman %D 2016 %K graph-based recommendation %R 10.1016/j.eswa.2016.09.013 %T Graph-based Collaborative Ranking %U http://arxiv.org/abs/1604.03147 %X Data sparsity, that is a common problem in neighbor-based collaborative filtering domain, usually complicates the process of item recommendation. This problem is more serious in collaborative ranking domain, in which calculating the users similarities and recommending items are based on ranking data. Some graph-based approaches have been proposed to address the data sparsity problem, but they suffer from two flaws. First, they fail to correctly model the users priorities, and second, they cannot be used when the only available data is a set of ranking instead of rating values. In this paper, we propose a novel graph-based approach, called GRank, that is designed for collaborative ranking domain. GRank can correctly model users priorities in a new tripartite graph structure, and analyze it to directly infer a recommendation list. The experimental results show a significant improvement in recommendation quality compared to the state of the art graph-based recommendation algorithms and other collaborative ranking techniques.

@misc{shams2016graphbased, abstract = {Data sparsity, that is a common problem in neighbor-based collaborative filtering domain, usually complicates the process of item recommendation. This problem is more serious in collaborative ranking domain, in which calculating the users similarities and recommending items are based on ranking data. Some graph-based approaches have been proposed to address the data sparsity problem, but they suffer from two flaws. First, they fail to correctly model the users priorities, and second, they cannot be used when the only available data is a set of ranking instead of rating values. In this paper, we propose a novel graph-based approach, called GRank, that is designed for collaborative ranking domain. GRank can correctly model users priorities in a new tripartite graph structure, and analyze it to directly infer a recommendation list. The experimental results show a significant improvement in recommendation quality compared to the state of the art graph-based recommendation algorithms and other collaborative ranking techniques.}, added-at = {2018-11-16T11:53:02.000+0100}, author = {Shams, Bita and Haratizadeh, Saman}, biburl = {https://www.bibsonomy.org/bibtex/25b22f7377e2cd95fef897f4563124cb6/e.fischer}, description = {[1604.03147] Graph-based Collaborative Ranking}, doi = {10.1016/j.eswa.2016.09.013}, interhash = {f97e66cd27836d9fcfcf041bdbdb5d9b}, intrahash = {5b22f7377e2cd95fef897f4563124cb6}, keywords = {graph-based recommendation}, note = {cite arxiv:1604.03147}, timestamp = {2018-11-16T11:53:02.000+0100}, title = {Graph-based Collaborative Ranking}, url = {http://arxiv.org/abs/1604.03147}, year = 2016 }