GDRF: An Innovative Graph-Based Rank Fusion Method for Enhancing Diversity in Image Metasearch - Solenir Lima Figuerêdo, José; Marreiros Maia, Ana Lúcia; Tripodi Calumby, Rodrigo (Journal of Information and Data Management, 2025)
Metasearch technique combines a set of ranked images retrieved with different search engines to build a unified ranking in order to improve relevance. For this purpose, rank aggregation methods have been widely used, which also can improve the result provide by ambiguous or underspecified queries through process named diversification. However, current aggregation methods assume that the input rankings are built only according to the relevance of the items, disregarding the inter-relationship between images in each ranking. Consequently, these methods tend to be inadequate for diversity-oriented retrieval. The aggregated ranking may not improve results, mainly when considered a diversity optimization. To address this problem, we propose a diversity-aware rank fusion method, which was validated in the context of diverse image metasearch. Our method was compared with several order-based and score-based aggregation methods. The experimental findings indicate that the proposed method significantly improves the overall diversity of metasearch results. This result demonstrates the potential of the proposed method and paves the way for further research to explore the development of new methods implementing new aware-diversity heuristics.