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An approach of a quantum-inspired document ranking algorithm by using feature selection methodology

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Abstract

The main goal of an information retrieval system (IR) is ranking. Several methodologies were adopted with the integration of computing and advanced applied systems. However, traditional techniques have low stability, and machine learning techniques suffer from feature selection (FS) problems for exponentially growing data. This study speculates that combining quadratic unconstrained binary optimization (QUBO) with quantum annealing (QA) enhances the FS during the ranking process. QA is applied to reduce noisy and redundant data from the various state-of-the-art datasets in the field of IR. This study used the LETOR dataset. LETOR is divided into two versions: LETOR 3.0 (30,000 scientific documents) and LETOR 4.0 (more than 25 million documents). QA reveals the requisite quantum behavior in the findings using a processing unit known as the quantum processing unit (QPU). QPU addresses FS issues written as QUBO optimization problems successfully. The FS problem was QUBO formulated utilizing three quadratic models. A comparison was also made between the linear and QPU solvers employed in the ranking procedure. Quantum-based solutions outperform traditional methods in addressing ranking issues. Compared to the standard algorithms (LTR and LamdaMART), our suggested technique based on QA yields a normalized discounted cumulative gain of 0.39 and 0.80, respectively. QA can address real-world issues by offering practical solutions. QUBO and the QA process have improved feature selection for the ranking process.

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  1. Department of Computer Science and Engineering, Presidency University, Bangalore, India

    Rupam Bhagawati

  2. Department of Computer Science and Engineering, Gitam (Deemed to be University), Bangalore, India

    Thiruselvan Subramanian

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  1. Rupam Bhagawati
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Bhagawati, R., Subramanian, T. An approach of a quantum-inspired document ranking algorithm by using feature selection methodology. Int. j. inf. tecnol. 15, 4041–4053 (2023). https://doi.org/10.1007/s41870-023-01543-w

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