NMFDIV: A Nonnegative Matrix Factorization Approach for Search Result Diversification on Attributed Networks

Search result diversification is effective way to tackle query ambiguity and enhance result novelty. In the context of large information networks, diversifying search result is also critical for further design of applications such as link prediction and citation recommendation. In previous work, this problem has mainly been tackled in a way of implicit query intent. To further enhance the performance, we propose an explicit search result diversification method that explicitly encode query intent and represent nodes as representation vectors by a novel nonnegative matrix factorization approach, and the diversity of the results node account for the query relevance and the novelty w.r.t. these vectors. To learn representation vectors for networks, we derive the multiplicative update rules to train the nonnegative matrix factorization model. Finally, we perform a comprehensive evaluation on our proposals with various baselines. Experimental results show the effectiveness of our proposed solution, and verify that attributes do help improve diversification performance.