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GenDeR: A Generic Diversified Ranking Algorithm
Jingrui He · Hanghang Tong · Qiaozhu Mei · Boleslaw K Szymanski

Thu Dec 06 02:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor

Diversified ranking is a fundamental task in machine learning. It is broadly applicable in many real world problems, e.g., information retrieval, team assembling, product search, etc. In this paper, we consider a generic setting where we aim to diversify the top-k ranking list based on an arbitrary relevance function and an arbitrary similarity function among all the examples. We formulate it as an optimization problem and show that in general it is NP-hard. Then, we show that for a large volume of the parameter space, the proposed objective function enjoys the diminishing returns property, which enables us to design a scalable, greedy algorithm to find the near-optimal solution. Experimental results on real data sets demonstrate the effectiveness of the proposed algorithm.

Author Information

Jingrui He (University of Illinois at Urbana-Champaign)
Hanghang Tong (University of Illinois at Urbana-Champaign)
Qiaozhu Mei (University of Michigan)
Boleslaw K Szymanski (RPI)

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