Integration of data fusion and reinforcement learning techniques for the rank-aggregation problem
Rank-aggregation or combining multiple ranked lists is the heart of meta-search engines in web information retrieval. In this paper, a novel rank-aggregation method is proposed, which utilizes both data fusion operators and reinforcement learning algorithms. Such integration enables us to use the compactness property of data fusion methods as well as the exploration and exploitation capabilities of reinforcement learning techniques. The proposed algorithm is a two-steps process. In the first step, ranked lists of local rankers are combined based on their mean average precisions with a variety of data fusion operators such as optimistic and pessimistic ordered weighted averaging (OWA) operators. This aggregation provides a compact representation of the utilized benchmark dataset. In the second step, a Markov decision process (MDP) model is defined for the aggregated data. This MDP enables us to apply reinforcement learning techniques such as Q-learning and SARSA for learning the best ranking. Experimentations on the LETOR4.0 benchmark dataset demonstrates that the proposed method outperforms baseline rank-aggregation methods such as Borda Count and the family of coset-permutation distance based stage-wise (CPS) rank-aggregation methods on P@n and NDCG@n evaluation criteria. The achieved improvement is especially more noticeable in the higher ranks in the final ranked list, which is usually more attractive to Web users.