Peer recommendation using negative relevance feedback

It is a challenging task to recommend a peer to a user based on the user’s requirement. Users may have expertise in multiple sub-domains, due to which peer recommendation is a nontrivial task. In this paper, we model peers as nodes in a graph and perform a community search. Weighted attributes are associated with every node in the graph. We propose two novel methods to compute the weights of the attributes. Relevance feedback is a popular technique used to improve the performance of retrieval systems. We propose to use negative relevance feedback in an attributed graph for peer recommendation. We use CL-tree for indexing the nodes in the graph. We compare the proposed system with the state-of-the-art on standard datasets, and our system outperforms the rival system.